parakeet-rs 0.4.2

// Generated by scripts/generate_burn.py from nemotron3_diar_v3.onnx with burn-onnx 0.22.0. Do not edit.
extern crate alloc;
use burn::prelude::*;
use burn::nn::LayerNorm;
use burn::nn::LayerNormConfig;
use burn::nn::Linear;
use burn::nn::LinearConfig;
use burn::nn::PaddingConfig1d;
use burn::nn::conv::Conv1d;
use burn::nn::conv::Conv1dConfig;
use burn::nn::pool::AvgPool1d;
use burn::nn::pool::AvgPool1dConfig;
use burn::tensor::TensorData;


#[derive(Module, Debug)]
pub struct Submodule1 {
    linear1: Linear,
    constant378: burn::module::Param<Tensor<1, Int>>,
    constant379: burn::module::Param<Tensor<1, Int>>,
    constant380: burn::module::Param<Tensor<1, Int>>,
    constant411: burn::module::Param<Tensor<1, Int>>,
    constant421: burn::module::Param<Tensor<1, Int>>,
    constant431: burn::module::Param<Tensor<1, Int>>,
    #[module(skip)]
    device: Device,
}
impl Submodule1 {
    #[allow(unused_variables)]
    pub fn new(device: &Device) -> Self {
        let linear1 = LinearConfig::new(1024, 512).with_bias(false).init(device);
        let constant378: burn::module::Param<Tensor<1, Int>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
                Int,
            >::from_data(
                burn::tensor::TensorData::from([8i64]),
                (device, burn::tensor::DType::I64),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant379: burn::module::Param<Tensor<1, Int>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
                Int,
            >::from_data(
                burn::tensor::TensorData::from([1i64]),
                (device, burn::tensor::DType::I64),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant380: burn::module::Param<Tensor<1, Int>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
                Int,
            >::from_data(
                burn::tensor::TensorData::from([8i64]),
                (device, burn::tensor::DType::I64),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant411: burn::module::Param<Tensor<1, Int>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
                Int,
            >::from_data(
                burn::tensor::TensorData::from([-1i64]),
                (device, burn::tensor::DType::I64),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant421: burn::module::Param<Tensor<1, Int>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
                Int,
            >::from_data(
                burn::tensor::TensorData::from([-1i64]),
                (device, burn::tensor::DType::I64),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant431: burn::module::Param<Tensor<1, Int>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
                Int,
            >::from_data(
                burn::tensor::TensorData::from([-1i64]),
                (device, burn::tensor::DType::I64),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        Self {
            linear1,
            constant378,
            constant379,
            constant380,
            constant411,
            constant421,
            constant431,
            device: device.clone(),
        }
    }
    #[allow(clippy::let_and_return, clippy::approx_constant)]
    pub fn forward(
        &self,
        chunk: Tensor<3>,
        chunk_lengths: Tensor<1, Int>,
        spkcache: Tensor<3>,
        fifo: Tensor<3>,
        spkcache_lengths: Tensor<1, Int>,
        fifo_lengths: Tensor<1, Int>,
    ) -> (Tensor<1, Int>, Tensor<3>, Tensor<3>, Tensor<1, Int>) {
        let shape1_out1: [i64; 3] = {
            let axes = &chunk.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather1_out1 = shape1_out1[0] as i64;
        let shape2_out1: [i64; 3] = {
            let axes = &chunk.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather2_out1 = shape2_out1[1] as i64;
        let shape3_out1: [i64; 3] = {
            let axes = &chunk.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather3_out1 = shape3_out1[2] as i64;
        let constant370_out1 = 8i64;
        let mod1_out1 = gather2_out1.rem_euclid(constant370_out1);
        let constant371_out1 = 8i64;
        let sub1_out1 = constant371_out1 - mod1_out1;
        let constant372_out1 = 8i64;
        let mod2_out1 = sub1_out1.rem_euclid(constant372_out1);
        let add1_out1 = gather2_out1 + mod2_out1;
        let constant373_out1 = 8i64;
        let div1_out1 = add1_out1 / constant373_out1;
        let cast1_out1 = div1_out1;
        let cast2_out1 = cast1_out1;
        let constant374_out1 = 8i64;
        let mul1_out1 = gather3_out1 * constant374_out1;
        let unsqueeze1_out1 = [gather1_out1 as i64];
        let unsqueeze2_out1 = [cast2_out1 as i64];
        let unsqueeze3_out1 = [mul1_out1 as i64];
        let concat1_out1: [i64; 3usize] = [
            &unsqueeze1_out1[..],
            &unsqueeze2_out1[..],
            &unsqueeze3_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape1_out1 = chunk.reshape(concat1_out1);
        let linear1_out1 = self.linear1.forward(reshape1_out1);
        let constant378_out1 = self.constant378.val();
        let add2_out1 = chunk_lengths.add(constant378_out1);
        let constant379_out1 = self.constant379.val();
        let sub2_out1 = add2_out1.sub(constant379_out1);
        let constant380_out1 = self.constant380.val();
        let div2_out1 = sub2_out1.div(constant380_out1);
        let cast3_out1 = div2_out1;
        let cast4_out1 = cast3_out1;
        let cast5_out1 = cast4_out1;
        let shape4_out1: [i64; 3] = {
            let axes = &spkcache.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather4_out1 = shape4_out1[1] as i64;
        let shape5_out1: [i64; 3] = {
            let axes = &fifo.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather5_out1 = shape5_out1[1] as i64;
        let add3_out1 = gather4_out1 + gather5_out1;
        let shape6_out1: [i64; 3] = {
            let axes = &linear1_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather6_out1 = shape6_out1[1] as i64;
        let add4_out1 = add3_out1 + gather6_out1;
        let shape7_out1: [i64; 3] = {
            let axes = &spkcache.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather7_out1 = shape7_out1[0] as i64;
        let shape8_out1: [i64; 3] = {
            let axes = &spkcache.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather8_out1 = shape8_out1[2] as i64;
        let unsqueeze4_out1: Tensor<2, Int> = spkcache_lengths
            .clone()
            .unsqueeze_dims::<2>(&[0]);
        let unsqueeze5_out1: Tensor<2, Int> = fifo_lengths
            .clone()
            .unsqueeze_dims::<2>(&[0]);
        let unsqueeze6_out1: Tensor<2, Int> = cast5_out1
            .clone()
            .unsqueeze_dims::<2>(&[0]);
        let concat2_out1 = burn::tensor::Tensor::cat(
            [unsqueeze4_out1, unsqueeze5_out1, unsqueeze6_out1].into(),
            0,
        );
        let reducesum1_out1 = {
            concat2_out1.sum_dim(0usize).squeeze_dims::<1usize>(&[0])
        };
        let mul2_out1 = gather7_out1 * add4_out1;
        let constant390_out1 = 1i64;
        let add5_out1 = mul2_out1 + constant390_out1;
        let unsqueeze7_out1 = [add5_out1 as i64];
        let unsqueeze8_out1 = [gather8_out1 as i64];
        let concat3_out1: [i64; 2usize] = [&unsqueeze7_out1[..], &unsqueeze8_out1[..]]
            .concat()
            .try_into()
            .unwrap();
        let constantofshape1_out1 = Tensor::<
            1,
        >::from_data(
                burn::tensor::TensorData::from([0f32 as f64]),
                (&self.device, burn::tensor::DType::F32),
            )
            .reshape([1, 1])
            .expand(concat3_out1);
        let unsqueeze9_out1 = [gather7_out1 as i64];
        let concat4_out1: [i64; 1usize] = [&unsqueeze9_out1[..]]
            .concat()
            .try_into()
            .unwrap();
        let constantofshape2_out1 = Tensor::<
            1,
            Int,
        >::from_data(
                burn::tensor::TensorData::from([0i64 as i64]),
                (&self.device, burn::tensor::DType::I64),
            )
            .reshape([1])
            .expand(concat4_out1);
        let cast6_out1 = gather7_out1;
        let range1_out1 = {
            let start = 0i64;
            let delta = 1i64;
            assert!(delta != 0);
            let n = (((cast6_out1 as i128 - start as i128) as f64) / delta as f64)
                .ceil()
                .max(0.0) as i64;
            Tensor::arange(0..n, &self.device)
                .cast(burn::tensor::DType::I64)
                .mul_scalar(delta)
                .add_scalar(start)
        };
        let unsqueeze10_out1: Tensor<2, Int> = range1_out1.unsqueeze_dims::<2>(&[1]);
        let mul3_out1 = unsqueeze10_out1.mul_scalar(add4_out1);
        let cast7_out1 = mul2_out1;
        let cast8_out1 = gather4_out1;
        let range2_out1 = {
            let start = 0i64;
            let delta = 1i64;
            assert!(delta != 0);
            let n = (((cast8_out1 as i128 - start as i128) as f64) / delta as f64)
                .ceil()
                .max(0.0) as i64;
            Tensor::arange(0..n, &self.device)
                .cast(burn::tensor::DType::I64)
                .mul_scalar(delta)
                .add_scalar(start)
        };
        let unsqueeze11_out1: Tensor<2, Int> = range2_out1.unsqueeze_dims::<2>(&[0]);
        let unsqueeze12_out1: Tensor<2, Int> = spkcache_lengths
            .clone()
            .unsqueeze_dims::<2>(&[1]);
        let less1_out1 = unsqueeze11_out1.clone().lower(unsqueeze12_out1);
        let unsqueeze13_out1: Tensor<2, Int> = constantofshape2_out1
            .clone()
            .unsqueeze_dims::<2>(&[1]);
        let add6_out1 = mul3_out1.clone().add(unsqueeze13_out1);
        let add7_out1 = add6_out1.add(unsqueeze11_out1);
        let where1_out1 = Tensor::<
            1,
            burn::tensor::Int,
        >::from_data(
                burn::tensor::TensorData::from([cast7_out1 as i64]),
                (&self.device, burn::tensor::DType::I64),
            )
            .reshape([1, 1])
            .mask_where(less1_out1, add7_out1);
        let reshape2_out1 = where1_out1.reshape([-1]);
        let unsqueeze14_out1 = [gather8_out1 as i64];
        let constant403_out1: [i64; 1] = [-1i64];
        let concat5_out1: [i64; 2usize] = [&constant403_out1[..], &unsqueeze14_out1[..]]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze15_out1 = [gather8_out1 as i64];
        let constant405_out1: [i64; 1] = [-1i64];
        let concat6_out1: [i64; 2usize] = [&constant405_out1[..], &unsqueeze15_out1[..]]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze16_out1 = [gather8_out1 as i64];
        let constant407_out1: [i64; 1] = [-1i64];
        let concat7_out1: [i64; 2usize] = [&constant407_out1[..], &unsqueeze16_out1[..]]
            .concat()
            .try_into()
            .unwrap();
        let reshape3_out1 = spkcache.reshape(concat5_out1);
        let unsqueeze17_out1: Tensor<2, Int> = reshape2_out1
            .clone()
            .unsqueeze_dims::<2>(&[1]);
        let shape9_out1: [i64; 2] = {
            let axes = &constantofshape1_out1.clone().dims()[0..2];
            let mut output = [0i64; 2];
            for i in 0..2 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let shape10_out1: [i64; 1] = {
            let axes = &reshape2_out1.dims()[0..1];
            let mut output = [0i64; 1];
            for i in 0..1 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let slice1_out1: [i64; 1] = shape9_out1[1..2].try_into().unwrap();
        let concat8_out1: [i64; 2usize] = [&shape10_out1[..], &slice1_out1[..]]
            .concat()
            .try_into()
            .unwrap();
        let shape11_out1: [i64; 1] = [2i64];
        let constantofshape3_out1 = Tensor::<
            1,
            Int,
        >::from_data(
                burn::tensor::TensorData::from([1i64 as i64]),
                (&self.device, burn::tensor::DType::I64),
            )
            .reshape([1])
            .expand(shape11_out1);
        let constant411_out1 = self.constant411.val();
        let mul4_out1 = constantofshape3_out1.clone().mul(constant411_out1);
        let equal1_out1 = Tensor::<
            1,
            burn::tensor::Int,
        >::from_data(
                burn::tensor::TensorData::from(&concat8_out1 as &[i64]),
                (&self.device, burn::tensor::DType::I64),
            )
            .equal(mul4_out1);
        let where2_out1 = Tensor::<
            1,
            burn::tensor::Int,
        >::from_data(
                burn::tensor::TensorData::from(&concat8_out1 as &[i64]),
                (&self.device, burn::tensor::DType::I64),
            )
            .mask_where(equal1_out1, constantofshape3_out1);
        let constant412_out1 = -1i64;
        let mul5_out1 = {
            let mut result = concat8_out1;
            let scalar = constant412_out1;
            for result_item in result.iter_mut() {
                *result_item = result_item.saturating_mul(scalar);
            }
            result
        };
        let max1_out1 = {
            let __lhs = concat8_out1;
            let __rhs = mul5_out1;
            core::array::from_fn::<i64, 2usize, _>(|__i| (__lhs[__i]).max(__rhs[__i]))
        };
        let expand1_out1 = {
            let onnx_shape: [i64; 2usize] = max1_out1;
            let input_dims = unsqueeze17_out1.dims();
            let mut shape = onnx_shape;
            #[allow(clippy::needless_range_loop)]
            for i in 0..2usize {
                let dim_offset = i;
                if shape[dim_offset] == 1 && input_dims[i] > 1 {
                    shape[dim_offset] = input_dims[i] as i64;
                }
            }
            unsqueeze17_out1.expand(shape)
        };
        let scatterelements1_out1 = {
            let (data, indices, updates) = (
                constantofshape1_out1,
                expand1_out1,
                reshape3_out1,
            );
            let axis_size = data.dims()[0] as i64;
            let indices = indices
                .cast(burn::tensor::DType::I64)
                .remainder_scalar(axis_size);
            let idx_dims = indices.dims();
            let data_dims = data.dims();
            let n: usize = idx_dims.iter().product();
            if n == 0 {
                data
            } else if (0..2).all(|d| d == 0 || idx_dims[d] == data_dims[d]) {
                data.scatter(0, indices, updates, burn::tensor::IndexingUpdateOp::Assign)
            } else {
                let mut strides = [1usize; 2];
                for d in (0..2 - 1).rev() {
                    strides[d] = strides[d + 1] * idx_dims[d + 1];
                }
                let flat = Tensor::<
                    1,
                    Int,
                >::arange(0..n as i64, (&self.device, burn::tensor::DType::I64));
                let mut columns: alloc::vec::Vec<Tensor<2, Int>> = alloc::vec::Vec::with_capacity(
                    2,
                );
                for d in 0..2 {
                    columns
                        .push(
                            if d == 0 {
                                indices.clone().reshape([n, 1])
                            } else {
                                flat.clone()
                                    .div_scalar(strides[d] as i64)
                                    .remainder_scalar(idx_dims[d] as i64)
                                    .reshape([n, 1])
                            },
                        );
                }
                let coordinates = Tensor::cat(columns, 1);
                data.scatter_nd(
                    coordinates,
                    updates.reshape([n]),
                    burn::tensor::IndexingUpdateOp::Assign,
                )
            }
        };
        let add8_out1 = constantofshape2_out1.add(spkcache_lengths);
        let cast9_out1 = gather5_out1;
        let range3_out1 = {
            let start = 0i64;
            let delta = 1i64;
            assert!(delta != 0);
            let n = (((cast9_out1 as i128 - start as i128) as f64) / delta as f64)
                .ceil()
                .max(0.0) as i64;
            Tensor::arange(0..n, &self.device)
                .cast(burn::tensor::DType::I64)
                .mul_scalar(delta)
                .add_scalar(start)
        };
        let unsqueeze18_out1: Tensor<2, Int> = range3_out1.unsqueeze_dims::<2>(&[0]);
        let unsqueeze19_out1: Tensor<2, Int> = fifo_lengths
            .clone()
            .unsqueeze_dims::<2>(&[1]);
        let less2_out1 = unsqueeze18_out1.clone().lower(unsqueeze19_out1);
        let unsqueeze20_out1: Tensor<2, Int> = add8_out1
            .clone()
            .unsqueeze_dims::<2>(&[1]);
        let add9_out1 = mul3_out1.clone().add(unsqueeze20_out1);
        let add10_out1 = add9_out1.add(unsqueeze18_out1);
        let where3_out1 = Tensor::<
            1,
            burn::tensor::Int,
        >::from_data(
                burn::tensor::TensorData::from([cast7_out1 as i64]),
                (&self.device, burn::tensor::DType::I64),
            )
            .reshape([1, 1])
            .mask_where(less2_out1, add10_out1);
        let reshape4_out1 = where3_out1.reshape([-1]);
        let reshape5_out1 = fifo.reshape(concat6_out1);
        let unsqueeze21_out1: Tensor<2, Int> = reshape4_out1
            .clone()
            .unsqueeze_dims::<2>(&[1]);
        let shape12_out1: [i64; 2] = {
            let axes = &scatterelements1_out1.clone().dims()[0..2];
            let mut output = [0i64; 2];
            for i in 0..2 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let shape13_out1: [i64; 1] = {
            let axes = &reshape4_out1.dims()[0..1];
            let mut output = [0i64; 1];
            for i in 0..1 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let slice2_out1: [i64; 1] = shape12_out1[1..2].try_into().unwrap();
        let concat9_out1: [i64; 2usize] = [&shape13_out1[..], &slice2_out1[..]]
            .concat()
            .try_into()
            .unwrap();
        let shape14_out1: [i64; 1] = [2i64];
        let constantofshape4_out1 = Tensor::<
            1,
            Int,
        >::from_data(
                burn::tensor::TensorData::from([1i64 as i64]),
                (&self.device, burn::tensor::DType::I64),
            )
            .reshape([1])
            .expand(shape14_out1);
        let constant421_out1 = self.constant421.val();
        let mul6_out1 = constantofshape4_out1.clone().mul(constant421_out1);
        let equal2_out1 = Tensor::<
            1,
            burn::tensor::Int,
        >::from_data(
                burn::tensor::TensorData::from(&concat9_out1 as &[i64]),
                (&self.device, burn::tensor::DType::I64),
            )
            .equal(mul6_out1);
        let where4_out1 = Tensor::<
            1,
            burn::tensor::Int,
        >::from_data(
                burn::tensor::TensorData::from(&concat9_out1 as &[i64]),
                (&self.device, burn::tensor::DType::I64),
            )
            .mask_where(equal2_out1, constantofshape4_out1);
        let constant422_out1 = -1i64;
        let mul7_out1 = {
            let mut result = concat9_out1;
            let scalar = constant422_out1;
            for result_item in result.iter_mut() {
                *result_item = result_item.saturating_mul(scalar);
            }
            result
        };
        let max2_out1 = {
            let __lhs = concat9_out1;
            let __rhs = mul7_out1;
            core::array::from_fn::<i64, 2usize, _>(|__i| (__lhs[__i]).max(__rhs[__i]))
        };
        let expand2_out1 = {
            let onnx_shape: [i64; 2usize] = max2_out1;
            let input_dims = unsqueeze21_out1.dims();
            let mut shape = onnx_shape;
            #[allow(clippy::needless_range_loop)]
            for i in 0..2usize {
                let dim_offset = i;
                if shape[dim_offset] == 1 && input_dims[i] > 1 {
                    shape[dim_offset] = input_dims[i] as i64;
                }
            }
            unsqueeze21_out1.expand(shape)
        };
        let scatterelements2_out1 = {
            let (data, indices, updates) = (
                scatterelements1_out1,
                expand2_out1,
                reshape5_out1,
            );
            let axis_size = data.dims()[0] as i64;
            let indices = indices
                .cast(burn::tensor::DType::I64)
                .remainder_scalar(axis_size);
            let idx_dims = indices.dims();
            let data_dims = data.dims();
            let n: usize = idx_dims.iter().product();
            if n == 0 {
                data
            } else if (0..2).all(|d| d == 0 || idx_dims[d] == data_dims[d]) {
                data.scatter(0, indices, updates, burn::tensor::IndexingUpdateOp::Assign)
            } else {
                let mut strides = [1usize; 2];
                for d in (0..2 - 1).rev() {
                    strides[d] = strides[d + 1] * idx_dims[d + 1];
                }
                let flat = Tensor::<
                    1,
                    Int,
                >::arange(0..n as i64, (&self.device, burn::tensor::DType::I64));
                let mut columns: alloc::vec::Vec<Tensor<2, Int>> = alloc::vec::Vec::with_capacity(
                    2,
                );
                for d in 0..2 {
                    columns
                        .push(
                            if d == 0 {
                                indices.clone().reshape([n, 1])
                            } else {
                                flat.clone()
                                    .div_scalar(strides[d] as i64)
                                    .remainder_scalar(idx_dims[d] as i64)
                                    .reshape([n, 1])
                            },
                        );
                }
                let coordinates = Tensor::cat(columns, 1);
                data.scatter_nd(
                    coordinates,
                    updates.reshape([n]),
                    burn::tensor::IndexingUpdateOp::Assign,
                )
            }
        };
        let add11_out1 = add8_out1.add(fifo_lengths);
        let cast10_out1 = gather6_out1;
        let range4_out1 = {
            let start = 0i64;
            let delta = 1i64;
            assert!(delta != 0);
            let n = (((cast10_out1 as i128 - start as i128) as f64) / delta as f64)
                .ceil()
                .max(0.0) as i64;
            Tensor::arange(0..n, &self.device)
                .cast(burn::tensor::DType::I64)
                .mul_scalar(delta)
                .add_scalar(start)
        };
        let unsqueeze22_out1: Tensor<2, Int> = range4_out1.unsqueeze_dims::<2>(&[0]);
        let unsqueeze23_out1: Tensor<2, Int> = cast5_out1
            .clone()
            .unsqueeze_dims::<2>(&[1]);
        let less3_out1 = unsqueeze22_out1.clone().lower(unsqueeze23_out1);
        let unsqueeze24_out1: Tensor<2, Int> = add11_out1.unsqueeze_dims::<2>(&[1]);
        let add12_out1 = mul3_out1.add(unsqueeze24_out1);
        let add13_out1 = add12_out1.add(unsqueeze22_out1);
        let where5_out1 = Tensor::<
            1,
            burn::tensor::Int,
        >::from_data(
                burn::tensor::TensorData::from([cast7_out1 as i64]),
                (&self.device, burn::tensor::DType::I64),
            )
            .reshape([1, 1])
            .mask_where(less3_out1, add13_out1);
        let reshape6_out1 = where5_out1.reshape([-1]);
        let reshape7_out1 = linear1_out1.clone().reshape(concat7_out1);
        let unsqueeze25_out1: Tensor<2, Int> = reshape6_out1
            .clone()
            .unsqueeze_dims::<2>(&[1]);
        let shape15_out1: [i64; 2] = {
            let axes = &scatterelements2_out1.clone().dims()[0..2];
            let mut output = [0i64; 2];
            for i in 0..2 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let shape16_out1: [i64; 1] = {
            let axes = &reshape6_out1.dims()[0..1];
            let mut output = [0i64; 1];
            for i in 0..1 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let slice3_out1: [i64; 1] = shape15_out1[1..2].try_into().unwrap();
        let concat10_out1: [i64; 2usize] = [&shape16_out1[..], &slice3_out1[..]]
            .concat()
            .try_into()
            .unwrap();
        let shape17_out1: [i64; 1] = [2i64];
        let constantofshape5_out1 = Tensor::<
            1,
            Int,
        >::from_data(
                burn::tensor::TensorData::from([1i64 as i64]),
                (&self.device, burn::tensor::DType::I64),
            )
            .reshape([1])
            .expand(shape17_out1);
        let constant431_out1 = self.constant431.val();
        let mul8_out1 = constantofshape5_out1.clone().mul(constant431_out1);
        let equal3_out1 = Tensor::<
            1,
            burn::tensor::Int,
        >::from_data(
                burn::tensor::TensorData::from(&concat10_out1 as &[i64]),
                (&self.device, burn::tensor::DType::I64),
            )
            .equal(mul8_out1);
        let where6_out1 = Tensor::<
            1,
            burn::tensor::Int,
        >::from_data(
                burn::tensor::TensorData::from(&concat10_out1 as &[i64]),
                (&self.device, burn::tensor::DType::I64),
            )
            .mask_where(equal3_out1, constantofshape5_out1);
        let constant432_out1 = -1i64;
        let mul9_out1 = {
            let mut result = concat10_out1;
            let scalar = constant432_out1;
            for result_item in result.iter_mut() {
                *result_item = result_item.saturating_mul(scalar);
            }
            result
        };
        let max3_out1 = {
            let __lhs = concat10_out1;
            let __rhs = mul9_out1;
            core::array::from_fn::<i64, 2usize, _>(|__i| (__lhs[__i]).max(__rhs[__i]))
        };
        let expand3_out1 = {
            let onnx_shape: [i64; 2usize] = max3_out1;
            let input_dims = unsqueeze25_out1.dims();
            let mut shape = onnx_shape;
            #[allow(clippy::needless_range_loop)]
            for i in 0..2usize {
                let dim_offset = i;
                if shape[dim_offset] == 1 && input_dims[i] > 1 {
                    shape[dim_offset] = input_dims[i] as i64;
                }
            }
            unsqueeze25_out1.expand(shape)
        };
        let scatterelements3_out1 = {
            let (data, indices, updates) = (
                scatterelements2_out1,
                expand3_out1,
                reshape7_out1,
            );
            let axis_size = data.dims()[0] as i64;
            let indices = indices
                .cast(burn::tensor::DType::I64)
                .remainder_scalar(axis_size);
            let idx_dims = indices.dims();
            let data_dims = data.dims();
            let n: usize = idx_dims.iter().product();
            if n == 0 {
                data
            } else if (0..2).all(|d| d == 0 || idx_dims[d] == data_dims[d]) {
                data.scatter(0, indices, updates, burn::tensor::IndexingUpdateOp::Assign)
            } else {
                let mut strides = [1usize; 2];
                for d in (0..2 - 1).rev() {
                    strides[d] = strides[d + 1] * idx_dims[d + 1];
                }
                let flat = Tensor::<
                    1,
                    Int,
                >::arange(0..n as i64, (&self.device, burn::tensor::DType::I64));
                let mut columns: alloc::vec::Vec<Tensor<2, Int>> = alloc::vec::Vec::with_capacity(
                    2,
                );
                for d in 0..2 {
                    columns
                        .push(
                            if d == 0 {
                                indices.clone().reshape([n, 1])
                            } else {
                                flat.clone()
                                    .div_scalar(strides[d] as i64)
                                    .remainder_scalar(idx_dims[d] as i64)
                                    .reshape([n, 1])
                            },
                        );
                }
                let coordinates = Tensor::cat(columns, 1);
                data.scatter_nd(
                    coordinates,
                    updates.reshape([n]),
                    burn::tensor::IndexingUpdateOp::Assign,
                )
            }
        };
        let unsqueeze26_out1 = [mul2_out1 as i64];
        let slice4_out1 = scatterelements3_out1.slice(s![0..unsqueeze26_out1[0], ..]);
        let unsqueeze27_out1 = [gather7_out1 as i64];
        let unsqueeze28_out1 = [add4_out1 as i64];
        let unsqueeze29_out1 = [gather8_out1 as i64];
        let concat11_out1: [i64; 3usize] = [
            &unsqueeze27_out1[..],
            &unsqueeze28_out1[..],
            &unsqueeze29_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape8_out1 = slice4_out1.reshape(concat11_out1);
        (reducesum1_out1, reshape8_out1, linear1_out1, cast5_out1)
    }
}
#[derive(Module, Debug)]
pub struct Submodule2 {
    layernormalization1: LayerNorm,
    layernormalization2: LayerNorm,
    linear2: Linear,
    constant461: burn::module::Param<Tensor<2>>,
    constant474: burn::module::Param<Tensor<2>>,
    constant517: burn::module::Param<Tensor<1>>,
    constant519: burn::module::Param<Tensor<1>>,
    constant518: burn::module::Param<Tensor<1>>,
    constant520: burn::module::Param<Tensor<1>>,
    linear3: Linear,
    #[module(skip)]
    device: Device,
}
impl Submodule2 {
    #[allow(unused_variables)]
    pub fn new(device: &Device) -> Self {
        let layernormalization1 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let layernormalization2 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear2 = LinearConfig::new(512, 1536).with_bias(false).init(device);
        let constant461: burn::module::Param<Tensor<2>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                2,
            >::zeros([5000, 64], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [5000, 64].into(),
        );
        let constant474: burn::module::Param<Tensor<2>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                2,
            >::zeros([5000, 64], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [5000, 64].into(),
        );
        let constant517: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant519: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant518: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant520: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let linear3 = LinearConfig::new(512, 512).with_bias(true).init(device);
        Self {
            layernormalization1,
            layernormalization2,
            linear2,
            constant461,
            constant474,
            constant517,
            constant519,
            constant518,
            constant520,
            linear3,
            device: device.clone(),
        }
    }
    #[allow(clippy::let_and_return, clippy::approx_constant)]
    pub fn forward(
        &self,
        reducesum1_out1: Tensor<1, Int>,
        reshape8_out1: Tensor<3>,
    ) -> (Tensor<3>, Tensor<2>, Tensor<2>, Tensor<4, Bool>, Tensor<1, Int>) {
        let cast11_out1 = reducesum1_out1;
        let layernormalization1_out1 = {
            let dtype = reshape8_out1.dtype();
            self.layernormalization1
                .forward(reshape8_out1.cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let shape18_out1: [i64; 3] = {
            let axes = &layernormalization1_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather9_out1 = shape18_out1[0] as i64;
        let shape19_out1: [i64; 3] = {
            let axes = &layernormalization1_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather10_out1 = shape19_out1[1] as i64;
        let cast12_out1 = gather9_out1;
        let range5_out1 = {
            let start = 0i64;
            let delta = 1i64;
            assert!(delta != 0);
            let n = (((cast12_out1 as i128 - start as i128) as f64) / delta as f64)
                .ceil()
                .max(0.0) as i64;
            Tensor::arange(0..n, &self.device)
                .cast(burn::tensor::DType::I64)
                .mul_scalar(delta)
                .add_scalar(start)
        };
        let reshape9_out1 = range5_out1.reshape([-1, 1, 1, 1]);
        let cast13_out1 = gather10_out1;
        let range6_out1 = {
            let start = 0i64;
            let delta = 1i64;
            assert!(delta != 0);
            let n = (((cast13_out1 as i128 - start as i128) as f64) / delta as f64)
                .ceil()
                .max(0.0) as i64;
            Tensor::arange(0..n, &self.device)
                .cast(burn::tensor::DType::I64)
                .mul_scalar(delta)
                .add_scalar(start)
        };
        let reshape10_out1 = range6_out1.reshape([1, 1, 1, -1]);
        let gather11_out1 = {
            let axis_size = cast11_out1.clone().dims()[0] as i64;
            let negative = reshape9_out1.clone().lower_elem(0i64);
            let corrected = reshape9_out1.clone() + axis_size;
            let indices = reshape9_out1.mask_where(negative, corrected);
            cast11_out1.clone().take::<4, 4>(0, indices)
        };
        let less4_out1 = reshape10_out1.lower(gather11_out1);
        let layernormalization2_out1 = {
            let dtype = layernormalization1_out1.clone().dtype();
            self.layernormalization2
                .forward(layernormalization1_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let shape20_out1: [i64; 3] = {
            let axes = &layernormalization2_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather12_out1 = shape20_out1[0] as i64;
        let shape21_out1: [i64; 3] = {
            let axes = &layernormalization2_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather13_out1 = shape21_out1[1] as i64;
        let linear2_out1 = self.linear2.forward(layernormalization2_out1);
        let unsqueeze30_out1 = [gather12_out1 as i64];
        let unsqueeze31_out1 = [gather13_out1 as i64];
        let constant454_out1: [i64; 1] = [64i64];
        let constant452_out1: [i64; 1] = [3i64];
        let constant453_out1: [i64; 1] = [8i64];
        let concat12_out1: [i64; 5usize] = [
            &unsqueeze30_out1[..],
            &unsqueeze31_out1[..],
            &constant452_out1[..],
            &constant453_out1[..],
            &constant454_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape11_out1 = linear2_out1.reshape(concat12_out1);
        let transpose1_out1 = reshape11_out1.permute([2, 0, 3, 1, 4]);
        let split_tensors = transpose1_out1.split_with_sizes([1, 1, 1].into(), 0);
        let [split1_out1, split1_out2, split1_out3] = split_tensors.try_into().unwrap();
        let squeeze1_out1 = split1_out1.squeeze_dims::<4>(&[0]);
        let squeeze2_out1 = split1_out2.squeeze_dims::<4>(&[0]);
        let squeeze3_out1 = split1_out3.squeeze_dims::<4>(&[0]);
        let shape22_out1: [i64; 4] = {
            let axes = &squeeze1_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather14_out1 = shape22_out1[2] as i64;
        let shape23_out1: [i64; 4] = {
            let axes = &squeeze2_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather15_out1 = shape23_out1[2] as i64;
        let sub3_out1 = gather15_out1 - gather14_out1;
        let unsqueeze32_out1 = [gather15_out1 as i64];
        let constant461_out1 = self.constant461.val();
        let slice5_out1 = constant461_out1.clone().slice(s![0..unsqueeze32_out1[0], ..]);
        let unsqueeze33_out1 = [gather15_out1 as i64];
        let constant469_out1: [i64; 1] = [64i64];
        let constant466_out1: [i64; 1] = [1i64];
        let constant467_out1: [i64; 1] = [1i64];
        let concat13_out1: [i64; 4usize] = [
            &constant466_out1[..],
            &constant467_out1[..],
            &unsqueeze33_out1[..],
            &constant469_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze34_out1 = [gather15_out1 as i64];
        let constant473_out1: [i64; 1] = [64i64];
        let constant470_out1: [i64; 1] = [1i64];
        let constant471_out1: [i64; 1] = [1i64];
        let concat14_out1: [i64; 4usize] = [
            &constant470_out1[..],
            &constant471_out1[..],
            &unsqueeze34_out1[..],
            &constant473_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape12_out1 = slice5_out1.reshape(concat13_out1);
        let unsqueeze35_out1 = [gather15_out1 as i64];
        let constant474_out1 = self.constant474.val();
        let slice6_out1 = constant474_out1.clone().slice(s![0..unsqueeze35_out1[0], ..]);
        let reshape13_out1 = slice6_out1.reshape(concat14_out1);
        let unsqueeze36_out1 = [sub3_out1 as i64];
        let unsqueeze37_out1 = [gather15_out1 as i64];
        let slice7_out1 = constant461_out1
            .clone()
            .slice(s![unsqueeze36_out1[0]..unsqueeze37_out1[0], ..]);
        let unsqueeze38_out1 = [gather14_out1 as i64];
        let constant486_out1: [i64; 1] = [64i64];
        let constant483_out1: [i64; 1] = [1i64];
        let constant484_out1: [i64; 1] = [1i64];
        let concat15_out1: [i64; 4usize] = [
            &constant483_out1[..],
            &constant484_out1[..],
            &unsqueeze38_out1[..],
            &constant486_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze39_out1 = [gather14_out1 as i64];
        let constant490_out1: [i64; 1] = [64i64];
        let constant487_out1: [i64; 1] = [1i64];
        let constant488_out1: [i64; 1] = [1i64];
        let concat16_out1: [i64; 4usize] = [
            &constant487_out1[..],
            &constant488_out1[..],
            &unsqueeze39_out1[..],
            &constant490_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape14_out1 = slice7_out1.reshape(concat15_out1);
        let unsqueeze40_out1 = [sub3_out1 as i64];
        let unsqueeze41_out1 = [gather15_out1 as i64];
        let slice8_out1 = constant474_out1
            .clone()
            .slice(s![unsqueeze40_out1[0]..unsqueeze41_out1[0], ..]);
        let reshape15_out1 = slice8_out1.reshape(concat16_out1);
        let cast14_out1 = reshape14_out1;
        let cast15_out1 = reshape15_out1;
        let mul10_out1 = squeeze1_out1.clone().mul(cast14_out1);
        let shape24_out1: [i64; 4] = {
            let axes = &squeeze1_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather16_out1 = shape24_out1[3] as i64;
        let constant496_out1 = 2i64;
        let div3_out1 = gather16_out1 / constant496_out1;
        let cast16_out1 = div3_out1;
        let cast17_out1 = cast16_out1;
        let unsqueeze42_out1 = [cast17_out1 as i64];
        let slice9_out1 = squeeze1_out1
            .clone()
            .slice(s![.., .., .., unsqueeze42_out1[0]..9223372036854775807]);
        let neg1_out1 = slice9_out1.neg();
        let unsqueeze43_out1 = [cast17_out1 as i64];
        let slice10_out1 = squeeze1_out1.slice(s![.., .., .., 0..unsqueeze43_out1[0]]);
        let concat17_out1 = burn::tensor::Tensor::cat(
            [neg1_out1, slice10_out1].into(),
            3,
        );
        let mul11_out1 = concat17_out1.mul(cast15_out1);
        let add14_out1 = mul10_out1.add(mul11_out1);
        let cast18_out1 = reshape12_out1;
        let cast19_out1 = reshape13_out1;
        let mul12_out1 = squeeze2_out1.clone().mul(cast18_out1);
        let shape25_out1: [i64; 4] = {
            let axes = &squeeze2_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather17_out1 = shape25_out1[3] as i64;
        let constant506_out1 = 2i64;
        let div4_out1 = gather17_out1 / constant506_out1;
        let cast20_out1 = div4_out1;
        let cast21_out1 = cast20_out1;
        let unsqueeze44_out1 = [cast21_out1 as i64];
        let slice11_out1 = squeeze2_out1
            .clone()
            .slice(s![.., .., .., unsqueeze44_out1[0]..9223372036854775807]);
        let neg2_out1 = slice11_out1.neg();
        let unsqueeze45_out1 = [cast21_out1 as i64];
        let slice12_out1 = squeeze2_out1.slice(s![.., .., .., 0..unsqueeze45_out1[0]]);
        let concat18_out1 = burn::tensor::Tensor::cat(
            [neg2_out1, slice12_out1].into(),
            3,
        );
        let mul13_out1 = concat18_out1.mul(cast19_out1);
        let add15_out1 = mul12_out1.add(mul13_out1);
        let shape26_out1: [i64; 4] = {
            let axes = &add14_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let slice13_out1: [i64; 1] = shape26_out1[3..4].try_into().unwrap();
        let cast22_out1 = {
            let shape_array = slice13_out1 as [i64; 1usize];
            let float_array: [f64; 1usize] = shape_array.map(|x| x as f64);
            Tensor::<
                1,
            >::from_data(
                TensorData::from(float_array),
                (&self.device, burn::tensor::DType::F32),
            )
        };
        let sqrt1_out1 = cast22_out1.sqrt();
        let constant517_out1 = self.constant517.val();
        let div5_out1 = constant517_out1.div(sqrt1_out1);
        let cast23_out1 = div5_out1;
        let transpose2_out1 = add15_out1.permute([0, 1, 3, 2]);
        let sqrt2_out1 = cast23_out1.clone().sqrt();
        let mul14_out1 = add14_out1
            .mul((sqrt2_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let sqrt3_out1 = cast23_out1.sqrt();
        let mul15_out1 = transpose2_out1
            .mul((sqrt3_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let matmul3_out1 = mul14_out1.matmul(mul15_out1);
        let constant519_out1 = self.constant519.val();
        let constant518_out1 = self.constant518.val();
        let where7_out1 = constant519_out1
            .unsqueeze_dims(&[0isize, 1isize, 2isize])
            .mask_where(
                less4_out1.clone(),
                constant518_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let add16_out1 = matmul3_out1.add(where7_out1);
        let softmax1_out1 = burn::tensor::activation::softmax(add16_out1, 3);
        let isnan1_out1 = softmax1_out1.clone().is_nan();
        let constant520_out1 = self.constant520.val();
        let where8_out1 = softmax1_out1
            .mask_where(
                isnan1_out1,
                constant520_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let matmul4_out1 = where8_out1.matmul(squeeze3_out1);
        let transpose3_out1 = matmul4_out1.permute([0, 2, 1, 3]);
        let unsqueeze46_out1 = [gather12_out1 as i64];
        let unsqueeze47_out1 = [gather13_out1 as i64];
        let constant523_out1: [i64; 1] = [512i64];
        let concat19_out1: [i64; 3usize] = [
            &unsqueeze46_out1[..],
            &unsqueeze47_out1[..],
            &constant523_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape16_out1 = transpose3_out1.reshape(concat19_out1);
        let linear3_out1 = self.linear3.forward(reshape16_out1);
        let add17_out1 = layernormalization1_out1.add(linear3_out1);
        (add17_out1, constant461_out1, constant474_out1, less4_out1, cast11_out1)
    }
}
#[derive(Module, Debug)]
pub struct Submodule3 {
    layernormalization3: LayerNorm,
    linear4: Linear,
    constant524: burn::module::Param<Tensor<1>>,
    constant525: burn::module::Param<Tensor<1>>,
    constant526: burn::module::Param<Tensor<1>>,
    linear5: Linear,
    layernormalization4: LayerNorm,
    linear6: Linear,
    constant594: burn::module::Param<Tensor<1>>,
    constant596: burn::module::Param<Tensor<1>>,
    constant595: burn::module::Param<Tensor<1>>,
    constant597: burn::module::Param<Tensor<1>>,
    linear7: Linear,
    #[module(skip)]
    device: Device,
}
impl Submodule3 {
    #[allow(unused_variables)]
    pub fn new(device: &Device) -> Self {
        let layernormalization3 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear4 = LinearConfig::new(512, 2048).with_bias(true).init(device);
        let constant524: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1.4142135381698608f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant525: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant526: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([0.5f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let linear5 = LinearConfig::new(2048, 512).with_bias(true).init(device);
        let layernormalization4 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear6 = LinearConfig::new(512, 1536).with_bias(false).init(device);
        let constant594: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant596: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant595: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant597: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let linear7 = LinearConfig::new(512, 512).with_bias(true).init(device);
        Self {
            layernormalization3,
            linear4,
            constant524,
            constant525,
            constant526,
            linear5,
            layernormalization4,
            linear6,
            constant594,
            constant596,
            constant595,
            constant597,
            linear7,
            device: device.clone(),
        }
    }
    #[allow(clippy::let_and_return, clippy::approx_constant)]
    pub fn forward(
        &self,
        add17_out1: Tensor<3>,
        constant461_out1: Tensor<2>,
        constant474_out1: Tensor<2>,
        less4_out1: Tensor<4, Bool>,
    ) -> Tensor<3> {
        let layernormalization3_out1 = {
            let dtype = add17_out1.clone().dtype();
            self.layernormalization3
                .forward(add17_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let linear4_out1 = self.linear4.forward(layernormalization3_out1);
        let constant524_out1 = self.constant524.val();
        let div6_out1 = linear4_out1
            .clone()
            .div((constant524_out1).unsqueeze_dims(&[0isize, 1isize]));
        let erf1_out1 = div6_out1.erf();
        let constant525_out1 = self.constant525.val();
        let add18_out1 = erf1_out1
            .add((constant525_out1).unsqueeze_dims(&[0isize, 1isize]));
        let mul16_out1 = linear4_out1.mul(add18_out1);
        let constant526_out1 = self.constant526.val();
        let mul17_out1 = mul16_out1
            .mul((constant526_out1).unsqueeze_dims(&[0isize, 1isize]));
        let linear5_out1 = self.linear5.forward(mul17_out1);
        let add19_out1 = add17_out1.add(linear5_out1);
        let layernormalization4_out1 = {
            let dtype = add19_out1.clone().dtype();
            self.layernormalization4
                .forward(add19_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let shape27_out1: [i64; 3] = {
            let axes = &layernormalization4_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather18_out1 = shape27_out1[0] as i64;
        let shape28_out1: [i64; 3] = {
            let axes = &layernormalization4_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather19_out1 = shape28_out1[1] as i64;
        let linear6_out1 = self.linear6.forward(layernormalization4_out1);
        let unsqueeze48_out1 = [gather18_out1 as i64];
        let unsqueeze49_out1 = [gather19_out1 as i64];
        let constant533_out1: [i64; 1] = [64i64];
        let constant531_out1: [i64; 1] = [3i64];
        let constant532_out1: [i64; 1] = [8i64];
        let concat20_out1: [i64; 5usize] = [
            &unsqueeze48_out1[..],
            &unsqueeze49_out1[..],
            &constant531_out1[..],
            &constant532_out1[..],
            &constant533_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape17_out1 = linear6_out1.reshape(concat20_out1);
        let transpose4_out1 = reshape17_out1.permute([2, 0, 3, 1, 4]);
        let split_tensors = transpose4_out1.split_with_sizes([1, 1, 1].into(), 0);
        let [split2_out1, split2_out2, split2_out3] = split_tensors.try_into().unwrap();
        let squeeze4_out1 = split2_out1.squeeze_dims::<4>(&[0]);
        let squeeze5_out1 = split2_out2.squeeze_dims::<4>(&[0]);
        let squeeze6_out1 = split2_out3.squeeze_dims::<4>(&[0]);
        let shape29_out1: [i64; 4] = {
            let axes = &squeeze4_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather20_out1 = shape29_out1[2] as i64;
        let shape30_out1: [i64; 4] = {
            let axes = &squeeze5_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather21_out1 = shape30_out1[2] as i64;
        let sub4_out1 = gather21_out1 - gather20_out1;
        let unsqueeze50_out1 = [gather21_out1 as i64];
        let slice14_out1 = constant461_out1
            .clone()
            .slice(s![0..unsqueeze50_out1[0], ..]);
        let unsqueeze51_out1 = [gather21_out1 as i64];
        let constant547_out1: [i64; 1] = [64i64];
        let constant544_out1: [i64; 1] = [1i64];
        let constant545_out1: [i64; 1] = [1i64];
        let concat21_out1: [i64; 4usize] = [
            &constant544_out1[..],
            &constant545_out1[..],
            &unsqueeze51_out1[..],
            &constant547_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze52_out1 = [gather21_out1 as i64];
        let constant551_out1: [i64; 1] = [64i64];
        let constant548_out1: [i64; 1] = [1i64];
        let constant549_out1: [i64; 1] = [1i64];
        let concat22_out1: [i64; 4usize] = [
            &constant548_out1[..],
            &constant549_out1[..],
            &unsqueeze52_out1[..],
            &constant551_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape18_out1 = slice14_out1.reshape(concat21_out1);
        let unsqueeze53_out1 = [gather21_out1 as i64];
        let slice15_out1 = constant474_out1
            .clone()
            .slice(s![0..unsqueeze53_out1[0], ..]);
        let reshape19_out1 = slice15_out1.reshape(concat22_out1);
        let unsqueeze54_out1 = [sub4_out1 as i64];
        let unsqueeze55_out1 = [gather21_out1 as i64];
        let slice16_out1 = constant461_out1
            .slice(s![unsqueeze54_out1[0]..unsqueeze55_out1[0], ..]);
        let unsqueeze56_out1 = [gather20_out1 as i64];
        let constant563_out1: [i64; 1] = [64i64];
        let constant560_out1: [i64; 1] = [1i64];
        let constant561_out1: [i64; 1] = [1i64];
        let concat23_out1: [i64; 4usize] = [
            &constant560_out1[..],
            &constant561_out1[..],
            &unsqueeze56_out1[..],
            &constant563_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze57_out1 = [gather20_out1 as i64];
        let constant567_out1: [i64; 1] = [64i64];
        let constant564_out1: [i64; 1] = [1i64];
        let constant565_out1: [i64; 1] = [1i64];
        let concat24_out1: [i64; 4usize] = [
            &constant564_out1[..],
            &constant565_out1[..],
            &unsqueeze57_out1[..],
            &constant567_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape20_out1 = slice16_out1.reshape(concat23_out1);
        let unsqueeze58_out1 = [sub4_out1 as i64];
        let unsqueeze59_out1 = [gather21_out1 as i64];
        let slice17_out1 = constant474_out1
            .slice(s![unsqueeze58_out1[0]..unsqueeze59_out1[0], ..]);
        let reshape21_out1 = slice17_out1.reshape(concat24_out1);
        let cast24_out1 = reshape20_out1;
        let cast25_out1 = reshape21_out1;
        let mul18_out1 = squeeze4_out1.clone().mul(cast24_out1);
        let shape31_out1: [i64; 4] = {
            let axes = &squeeze4_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather22_out1 = shape31_out1[3] as i64;
        let constant573_out1 = 2i64;
        let div7_out1 = gather22_out1 / constant573_out1;
        let cast26_out1 = div7_out1;
        let cast27_out1 = cast26_out1;
        let unsqueeze60_out1 = [cast27_out1 as i64];
        let slice18_out1 = squeeze4_out1
            .clone()
            .slice(s![.., .., .., unsqueeze60_out1[0]..9223372036854775807]);
        let neg3_out1 = slice18_out1.neg();
        let unsqueeze61_out1 = [cast27_out1 as i64];
        let slice19_out1 = squeeze4_out1.slice(s![.., .., .., 0..unsqueeze61_out1[0]]);
        let concat25_out1 = burn::tensor::Tensor::cat(
            [neg3_out1, slice19_out1].into(),
            3,
        );
        let mul19_out1 = concat25_out1.mul(cast25_out1);
        let add20_out1 = mul18_out1.add(mul19_out1);
        let cast28_out1 = reshape18_out1;
        let cast29_out1 = reshape19_out1;
        let mul20_out1 = squeeze5_out1.clone().mul(cast28_out1);
        let shape32_out1: [i64; 4] = {
            let axes = &squeeze5_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather23_out1 = shape32_out1[3] as i64;
        let constant583_out1 = 2i64;
        let div8_out1 = gather23_out1 / constant583_out1;
        let cast30_out1 = div8_out1;
        let cast31_out1 = cast30_out1;
        let unsqueeze62_out1 = [cast31_out1 as i64];
        let slice20_out1 = squeeze5_out1
            .clone()
            .slice(s![.., .., .., unsqueeze62_out1[0]..9223372036854775807]);
        let neg4_out1 = slice20_out1.neg();
        let unsqueeze63_out1 = [cast31_out1 as i64];
        let slice21_out1 = squeeze5_out1.slice(s![.., .., .., 0..unsqueeze63_out1[0]]);
        let concat26_out1 = burn::tensor::Tensor::cat(
            [neg4_out1, slice21_out1].into(),
            3,
        );
        let mul21_out1 = concat26_out1.mul(cast29_out1);
        let add21_out1 = mul20_out1.add(mul21_out1);
        let shape33_out1: [i64; 4] = {
            let axes = &add20_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let slice22_out1: [i64; 1] = shape33_out1[3..4].try_into().unwrap();
        let cast32_out1 = {
            let shape_array = slice22_out1 as [i64; 1usize];
            let float_array: [f64; 1usize] = shape_array.map(|x| x as f64);
            Tensor::<
                1,
            >::from_data(
                TensorData::from(float_array),
                (&self.device, burn::tensor::DType::F32),
            )
        };
        let sqrt4_out1 = cast32_out1.sqrt();
        let constant594_out1 = self.constant594.val();
        let div9_out1 = constant594_out1.div(sqrt4_out1);
        let cast33_out1 = div9_out1;
        let transpose5_out1 = add21_out1.permute([0, 1, 3, 2]);
        let sqrt5_out1 = cast33_out1.clone().sqrt();
        let mul22_out1 = add20_out1
            .mul((sqrt5_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let sqrt6_out1 = cast33_out1.sqrt();
        let mul23_out1 = transpose5_out1
            .mul((sqrt6_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let matmul9_out1 = mul22_out1.matmul(mul23_out1);
        let constant596_out1 = self.constant596.val();
        let constant595_out1 = self.constant595.val();
        let where9_out1 = constant596_out1
            .unsqueeze_dims(&[0isize, 1isize, 2isize])
            .mask_where(
                less4_out1,
                constant595_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let add22_out1 = matmul9_out1.add(where9_out1);
        let softmax2_out1 = burn::tensor::activation::softmax(add22_out1, 3);
        let isnan2_out1 = softmax2_out1.clone().is_nan();
        let constant597_out1 = self.constant597.val();
        let where10_out1 = softmax2_out1
            .mask_where(
                isnan2_out1,
                constant597_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let matmul10_out1 = where10_out1.matmul(squeeze6_out1);
        let transpose6_out1 = matmul10_out1.permute([0, 2, 1, 3]);
        let unsqueeze64_out1 = [gather18_out1 as i64];
        let unsqueeze65_out1 = [gather19_out1 as i64];
        let constant600_out1: [i64; 1] = [512i64];
        let concat27_out1: [i64; 3usize] = [
            &unsqueeze64_out1[..],
            &unsqueeze65_out1[..],
            &constant600_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape22_out1 = transpose6_out1.reshape(concat27_out1);
        let linear7_out1 = self.linear7.forward(reshape22_out1);
        let add23_out1 = add19_out1.add(linear7_out1);
        add23_out1
    }
}
#[derive(Module, Debug)]
pub struct Submodule4 {
    layernormalization5: LayerNorm,
    linear8: Linear,
    constant601: burn::module::Param<Tensor<1>>,
    constant602: burn::module::Param<Tensor<1>>,
    constant603: burn::module::Param<Tensor<1>>,
    linear9: Linear,
    layernormalization6: LayerNorm,
    linear10: Linear,
    constant671: burn::module::Param<Tensor<1>>,
    constant673: burn::module::Param<Tensor<1>>,
    constant672: burn::module::Param<Tensor<1>>,
    constant674: burn::module::Param<Tensor<1>>,
    linear11: Linear,
    #[module(skip)]
    device: Device,
}
impl Submodule4 {
    #[allow(unused_variables)]
    pub fn new(device: &Device) -> Self {
        let layernormalization5 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear8 = LinearConfig::new(512, 2048).with_bias(true).init(device);
        let constant601: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1.4142135381698608f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant602: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant603: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([0.5f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let linear9 = LinearConfig::new(2048, 512).with_bias(true).init(device);
        let layernormalization6 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear10 = LinearConfig::new(512, 1536).with_bias(false).init(device);
        let constant671: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant673: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant672: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant674: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let linear11 = LinearConfig::new(512, 512).with_bias(true).init(device);
        Self {
            layernormalization5,
            linear8,
            constant601,
            constant602,
            constant603,
            linear9,
            layernormalization6,
            linear10,
            constant671,
            constant673,
            constant672,
            constant674,
            linear11,
            device: device.clone(),
        }
    }
    #[allow(clippy::let_and_return, clippy::approx_constant)]
    pub fn forward(
        &self,
        add23_out1: Tensor<3>,
        constant461_out1: Tensor<2>,
        constant474_out1: Tensor<2>,
        less4_out1: Tensor<4, Bool>,
    ) -> Tensor<3> {
        let layernormalization5_out1 = {
            let dtype = add23_out1.clone().dtype();
            self.layernormalization5
                .forward(add23_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let linear8_out1 = self.linear8.forward(layernormalization5_out1);
        let constant601_out1 = self.constant601.val();
        let div10_out1 = linear8_out1
            .clone()
            .div((constant601_out1).unsqueeze_dims(&[0isize, 1isize]));
        let erf2_out1 = div10_out1.erf();
        let constant602_out1 = self.constant602.val();
        let add24_out1 = erf2_out1
            .add((constant602_out1).unsqueeze_dims(&[0isize, 1isize]));
        let mul24_out1 = linear8_out1.mul(add24_out1);
        let constant603_out1 = self.constant603.val();
        let mul25_out1 = mul24_out1
            .mul((constant603_out1).unsqueeze_dims(&[0isize, 1isize]));
        let linear9_out1 = self.linear9.forward(mul25_out1);
        let add25_out1 = add23_out1.add(linear9_out1);
        let layernormalization6_out1 = {
            let dtype = add25_out1.clone().dtype();
            self.layernormalization6
                .forward(add25_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let shape34_out1: [i64; 3] = {
            let axes = &layernormalization6_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather24_out1 = shape34_out1[0] as i64;
        let shape35_out1: [i64; 3] = {
            let axes = &layernormalization6_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather25_out1 = shape35_out1[1] as i64;
        let linear10_out1 = self.linear10.forward(layernormalization6_out1);
        let unsqueeze66_out1 = [gather24_out1 as i64];
        let unsqueeze67_out1 = [gather25_out1 as i64];
        let constant610_out1: [i64; 1] = [64i64];
        let constant608_out1: [i64; 1] = [3i64];
        let constant609_out1: [i64; 1] = [8i64];
        let concat28_out1: [i64; 5usize] = [
            &unsqueeze66_out1[..],
            &unsqueeze67_out1[..],
            &constant608_out1[..],
            &constant609_out1[..],
            &constant610_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape23_out1 = linear10_out1.reshape(concat28_out1);
        let transpose7_out1 = reshape23_out1.permute([2, 0, 3, 1, 4]);
        let split_tensors = transpose7_out1.split_with_sizes([1, 1, 1].into(), 0);
        let [split3_out1, split3_out2, split3_out3] = split_tensors.try_into().unwrap();
        let squeeze7_out1 = split3_out1.squeeze_dims::<4>(&[0]);
        let squeeze8_out1 = split3_out2.squeeze_dims::<4>(&[0]);
        let squeeze9_out1 = split3_out3.squeeze_dims::<4>(&[0]);
        let shape36_out1: [i64; 4] = {
            let axes = &squeeze7_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather26_out1 = shape36_out1[2] as i64;
        let shape37_out1: [i64; 4] = {
            let axes = &squeeze8_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather27_out1 = shape37_out1[2] as i64;
        let sub5_out1 = gather27_out1 - gather26_out1;
        let unsqueeze68_out1 = [gather27_out1 as i64];
        let slice23_out1 = constant461_out1
            .clone()
            .slice(s![0..unsqueeze68_out1[0], ..]);
        let unsqueeze69_out1 = [gather27_out1 as i64];
        let constant624_out1: [i64; 1] = [64i64];
        let constant621_out1: [i64; 1] = [1i64];
        let constant622_out1: [i64; 1] = [1i64];
        let concat29_out1: [i64; 4usize] = [
            &constant621_out1[..],
            &constant622_out1[..],
            &unsqueeze69_out1[..],
            &constant624_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze70_out1 = [gather27_out1 as i64];
        let constant628_out1: [i64; 1] = [64i64];
        let constant625_out1: [i64; 1] = [1i64];
        let constant626_out1: [i64; 1] = [1i64];
        let concat30_out1: [i64; 4usize] = [
            &constant625_out1[..],
            &constant626_out1[..],
            &unsqueeze70_out1[..],
            &constant628_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape24_out1 = slice23_out1.reshape(concat29_out1);
        let unsqueeze71_out1 = [gather27_out1 as i64];
        let slice24_out1 = constant474_out1
            .clone()
            .slice(s![0..unsqueeze71_out1[0], ..]);
        let reshape25_out1 = slice24_out1.reshape(concat30_out1);
        let unsqueeze72_out1 = [sub5_out1 as i64];
        let unsqueeze73_out1 = [gather27_out1 as i64];
        let slice25_out1 = constant461_out1
            .slice(s![unsqueeze72_out1[0]..unsqueeze73_out1[0], ..]);
        let unsqueeze74_out1 = [gather26_out1 as i64];
        let constant640_out1: [i64; 1] = [64i64];
        let constant637_out1: [i64; 1] = [1i64];
        let constant638_out1: [i64; 1] = [1i64];
        let concat31_out1: [i64; 4usize] = [
            &constant637_out1[..],
            &constant638_out1[..],
            &unsqueeze74_out1[..],
            &constant640_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze75_out1 = [gather26_out1 as i64];
        let constant644_out1: [i64; 1] = [64i64];
        let constant641_out1: [i64; 1] = [1i64];
        let constant642_out1: [i64; 1] = [1i64];
        let concat32_out1: [i64; 4usize] = [
            &constant641_out1[..],
            &constant642_out1[..],
            &unsqueeze75_out1[..],
            &constant644_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape26_out1 = slice25_out1.reshape(concat31_out1);
        let unsqueeze76_out1 = [sub5_out1 as i64];
        let unsqueeze77_out1 = [gather27_out1 as i64];
        let slice26_out1 = constant474_out1
            .slice(s![unsqueeze76_out1[0]..unsqueeze77_out1[0], ..]);
        let reshape27_out1 = slice26_out1.reshape(concat32_out1);
        let cast34_out1 = reshape26_out1;
        let cast35_out1 = reshape27_out1;
        let mul26_out1 = squeeze7_out1.clone().mul(cast34_out1);
        let shape38_out1: [i64; 4] = {
            let axes = &squeeze7_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather28_out1 = shape38_out1[3] as i64;
        let constant650_out1 = 2i64;
        let div11_out1 = gather28_out1 / constant650_out1;
        let cast36_out1 = div11_out1;
        let cast37_out1 = cast36_out1;
        let unsqueeze78_out1 = [cast37_out1 as i64];
        let slice27_out1 = squeeze7_out1
            .clone()
            .slice(s![.., .., .., unsqueeze78_out1[0]..9223372036854775807]);
        let neg5_out1 = slice27_out1.neg();
        let unsqueeze79_out1 = [cast37_out1 as i64];
        let slice28_out1 = squeeze7_out1.slice(s![.., .., .., 0..unsqueeze79_out1[0]]);
        let concat33_out1 = burn::tensor::Tensor::cat(
            [neg5_out1, slice28_out1].into(),
            3,
        );
        let mul27_out1 = concat33_out1.mul(cast35_out1);
        let add26_out1 = mul26_out1.add(mul27_out1);
        let cast38_out1 = reshape24_out1;
        let cast39_out1 = reshape25_out1;
        let mul28_out1 = squeeze8_out1.clone().mul(cast38_out1);
        let shape39_out1: [i64; 4] = {
            let axes = &squeeze8_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather29_out1 = shape39_out1[3] as i64;
        let constant660_out1 = 2i64;
        let div12_out1 = gather29_out1 / constant660_out1;
        let cast40_out1 = div12_out1;
        let cast41_out1 = cast40_out1;
        let unsqueeze80_out1 = [cast41_out1 as i64];
        let slice29_out1 = squeeze8_out1
            .clone()
            .slice(s![.., .., .., unsqueeze80_out1[0]..9223372036854775807]);
        let neg6_out1 = slice29_out1.neg();
        let unsqueeze81_out1 = [cast41_out1 as i64];
        let slice30_out1 = squeeze8_out1.slice(s![.., .., .., 0..unsqueeze81_out1[0]]);
        let concat34_out1 = burn::tensor::Tensor::cat(
            [neg6_out1, slice30_out1].into(),
            3,
        );
        let mul29_out1 = concat34_out1.mul(cast39_out1);
        let add27_out1 = mul28_out1.add(mul29_out1);
        let shape40_out1: [i64; 4] = {
            let axes = &add26_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let slice31_out1: [i64; 1] = shape40_out1[3..4].try_into().unwrap();
        let cast42_out1 = {
            let shape_array = slice31_out1 as [i64; 1usize];
            let float_array: [f64; 1usize] = shape_array.map(|x| x as f64);
            Tensor::<
                1,
            >::from_data(
                TensorData::from(float_array),
                (&self.device, burn::tensor::DType::F32),
            )
        };
        let sqrt7_out1 = cast42_out1.sqrt();
        let constant671_out1 = self.constant671.val();
        let div13_out1 = constant671_out1.div(sqrt7_out1);
        let cast43_out1 = div13_out1;
        let transpose8_out1 = add27_out1.permute([0, 1, 3, 2]);
        let sqrt8_out1 = cast43_out1.clone().sqrt();
        let mul30_out1 = add26_out1
            .mul((sqrt8_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let sqrt9_out1 = cast43_out1.sqrt();
        let mul31_out1 = transpose8_out1
            .mul((sqrt9_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let matmul15_out1 = mul30_out1.matmul(mul31_out1);
        let constant673_out1 = self.constant673.val();
        let constant672_out1 = self.constant672.val();
        let where11_out1 = constant673_out1
            .unsqueeze_dims(&[0isize, 1isize, 2isize])
            .mask_where(
                less4_out1,
                constant672_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let add28_out1 = matmul15_out1.add(where11_out1);
        let softmax3_out1 = burn::tensor::activation::softmax(add28_out1, 3);
        let isnan3_out1 = softmax3_out1.clone().is_nan();
        let constant674_out1 = self.constant674.val();
        let where12_out1 = softmax3_out1
            .mask_where(
                isnan3_out1,
                constant674_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let matmul16_out1 = where12_out1.matmul(squeeze9_out1);
        let transpose9_out1 = matmul16_out1.permute([0, 2, 1, 3]);
        let unsqueeze82_out1 = [gather24_out1 as i64];
        let unsqueeze83_out1 = [gather25_out1 as i64];
        let constant677_out1: [i64; 1] = [512i64];
        let concat35_out1: [i64; 3usize] = [
            &unsqueeze82_out1[..],
            &unsqueeze83_out1[..],
            &constant677_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape28_out1 = transpose9_out1.reshape(concat35_out1);
        let linear11_out1 = self.linear11.forward(reshape28_out1);
        let add29_out1 = add25_out1.add(linear11_out1);
        add29_out1
    }
}
#[derive(Module, Debug)]
pub struct Submodule5 {
    layernormalization7: LayerNorm,
    linear12: Linear,
    constant678: burn::module::Param<Tensor<1>>,
    constant679: burn::module::Param<Tensor<1>>,
    constant680: burn::module::Param<Tensor<1>>,
    linear13: Linear,
    layernormalization8: LayerNorm,
    linear14: Linear,
    constant748: burn::module::Param<Tensor<1>>,
    constant750: burn::module::Param<Tensor<1>>,
    constant749: burn::module::Param<Tensor<1>>,
    constant751: burn::module::Param<Tensor<1>>,
    linear15: Linear,
    #[module(skip)]
    device: Device,
}
impl Submodule5 {
    #[allow(unused_variables)]
    pub fn new(device: &Device) -> Self {
        let layernormalization7 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear12 = LinearConfig::new(512, 2048).with_bias(true).init(device);
        let constant678: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1.4142135381698608f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant679: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant680: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([0.5f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let linear13 = LinearConfig::new(2048, 512).with_bias(true).init(device);
        let layernormalization8 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear14 = LinearConfig::new(512, 1536).with_bias(false).init(device);
        let constant748: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant750: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant749: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant751: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let linear15 = LinearConfig::new(512, 512).with_bias(true).init(device);
        Self {
            layernormalization7,
            linear12,
            constant678,
            constant679,
            constant680,
            linear13,
            layernormalization8,
            linear14,
            constant748,
            constant750,
            constant749,
            constant751,
            linear15,
            device: device.clone(),
        }
    }
    #[allow(clippy::let_and_return, clippy::approx_constant)]
    pub fn forward(
        &self,
        add29_out1: Tensor<3>,
        constant461_out1: Tensor<2>,
        constant474_out1: Tensor<2>,
        less4_out1: Tensor<4, Bool>,
    ) -> Tensor<3> {
        let layernormalization7_out1 = {
            let dtype = add29_out1.clone().dtype();
            self.layernormalization7
                .forward(add29_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let linear12_out1 = self.linear12.forward(layernormalization7_out1);
        let constant678_out1 = self.constant678.val();
        let div14_out1 = linear12_out1
            .clone()
            .div((constant678_out1).unsqueeze_dims(&[0isize, 1isize]));
        let erf3_out1 = div14_out1.erf();
        let constant679_out1 = self.constant679.val();
        let add30_out1 = erf3_out1
            .add((constant679_out1).unsqueeze_dims(&[0isize, 1isize]));
        let mul32_out1 = linear12_out1.mul(add30_out1);
        let constant680_out1 = self.constant680.val();
        let mul33_out1 = mul32_out1
            .mul((constant680_out1).unsqueeze_dims(&[0isize, 1isize]));
        let linear13_out1 = self.linear13.forward(mul33_out1);
        let add31_out1 = add29_out1.add(linear13_out1);
        let layernormalization8_out1 = {
            let dtype = add31_out1.clone().dtype();
            self.layernormalization8
                .forward(add31_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let shape41_out1: [i64; 3] = {
            let axes = &layernormalization8_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather30_out1 = shape41_out1[0] as i64;
        let shape42_out1: [i64; 3] = {
            let axes = &layernormalization8_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather31_out1 = shape42_out1[1] as i64;
        let linear14_out1 = self.linear14.forward(layernormalization8_out1);
        let unsqueeze84_out1 = [gather30_out1 as i64];
        let unsqueeze85_out1 = [gather31_out1 as i64];
        let constant687_out1: [i64; 1] = [64i64];
        let constant685_out1: [i64; 1] = [3i64];
        let constant686_out1: [i64; 1] = [8i64];
        let concat36_out1: [i64; 5usize] = [
            &unsqueeze84_out1[..],
            &unsqueeze85_out1[..],
            &constant685_out1[..],
            &constant686_out1[..],
            &constant687_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape29_out1 = linear14_out1.reshape(concat36_out1);
        let transpose10_out1 = reshape29_out1.permute([2, 0, 3, 1, 4]);
        let split_tensors = transpose10_out1.split_with_sizes([1, 1, 1].into(), 0);
        let [split4_out1, split4_out2, split4_out3] = split_tensors.try_into().unwrap();
        let squeeze10_out1 = split4_out1.squeeze_dims::<4>(&[0]);
        let squeeze11_out1 = split4_out2.squeeze_dims::<4>(&[0]);
        let squeeze12_out1 = split4_out3.squeeze_dims::<4>(&[0]);
        let shape43_out1: [i64; 4] = {
            let axes = &squeeze10_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather32_out1 = shape43_out1[2] as i64;
        let shape44_out1: [i64; 4] = {
            let axes = &squeeze11_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather33_out1 = shape44_out1[2] as i64;
        let sub6_out1 = gather33_out1 - gather32_out1;
        let unsqueeze86_out1 = [gather33_out1 as i64];
        let slice32_out1 = constant461_out1
            .clone()
            .slice(s![0..unsqueeze86_out1[0], ..]);
        let unsqueeze87_out1 = [gather33_out1 as i64];
        let constant701_out1: [i64; 1] = [64i64];
        let constant698_out1: [i64; 1] = [1i64];
        let constant699_out1: [i64; 1] = [1i64];
        let concat37_out1: [i64; 4usize] = [
            &constant698_out1[..],
            &constant699_out1[..],
            &unsqueeze87_out1[..],
            &constant701_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze88_out1 = [gather33_out1 as i64];
        let constant705_out1: [i64; 1] = [64i64];
        let constant702_out1: [i64; 1] = [1i64];
        let constant703_out1: [i64; 1] = [1i64];
        let concat38_out1: [i64; 4usize] = [
            &constant702_out1[..],
            &constant703_out1[..],
            &unsqueeze88_out1[..],
            &constant705_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape30_out1 = slice32_out1.reshape(concat37_out1);
        let unsqueeze89_out1 = [gather33_out1 as i64];
        let slice33_out1 = constant474_out1
            .clone()
            .slice(s![0..unsqueeze89_out1[0], ..]);
        let reshape31_out1 = slice33_out1.reshape(concat38_out1);
        let unsqueeze90_out1 = [sub6_out1 as i64];
        let unsqueeze91_out1 = [gather33_out1 as i64];
        let slice34_out1 = constant461_out1
            .slice(s![unsqueeze90_out1[0]..unsqueeze91_out1[0], ..]);
        let unsqueeze92_out1 = [gather32_out1 as i64];
        let constant717_out1: [i64; 1] = [64i64];
        let constant714_out1: [i64; 1] = [1i64];
        let constant715_out1: [i64; 1] = [1i64];
        let concat39_out1: [i64; 4usize] = [
            &constant714_out1[..],
            &constant715_out1[..],
            &unsqueeze92_out1[..],
            &constant717_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze93_out1 = [gather32_out1 as i64];
        let constant721_out1: [i64; 1] = [64i64];
        let constant718_out1: [i64; 1] = [1i64];
        let constant719_out1: [i64; 1] = [1i64];
        let concat40_out1: [i64; 4usize] = [
            &constant718_out1[..],
            &constant719_out1[..],
            &unsqueeze93_out1[..],
            &constant721_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape32_out1 = slice34_out1.reshape(concat39_out1);
        let unsqueeze94_out1 = [sub6_out1 as i64];
        let unsqueeze95_out1 = [gather33_out1 as i64];
        let slice35_out1 = constant474_out1
            .slice(s![unsqueeze94_out1[0]..unsqueeze95_out1[0], ..]);
        let reshape33_out1 = slice35_out1.reshape(concat40_out1);
        let cast44_out1 = reshape32_out1;
        let cast45_out1 = reshape33_out1;
        let mul34_out1 = squeeze10_out1.clone().mul(cast44_out1);
        let shape45_out1: [i64; 4] = {
            let axes = &squeeze10_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather34_out1 = shape45_out1[3] as i64;
        let constant727_out1 = 2i64;
        let div15_out1 = gather34_out1 / constant727_out1;
        let cast46_out1 = div15_out1;
        let cast47_out1 = cast46_out1;
        let unsqueeze96_out1 = [cast47_out1 as i64];
        let slice36_out1 = squeeze10_out1
            .clone()
            .slice(s![.., .., .., unsqueeze96_out1[0]..9223372036854775807]);
        let neg7_out1 = slice36_out1.neg();
        let unsqueeze97_out1 = [cast47_out1 as i64];
        let slice37_out1 = squeeze10_out1.slice(s![.., .., .., 0..unsqueeze97_out1[0]]);
        let concat41_out1 = burn::tensor::Tensor::cat(
            [neg7_out1, slice37_out1].into(),
            3,
        );
        let mul35_out1 = concat41_out1.mul(cast45_out1);
        let add32_out1 = mul34_out1.add(mul35_out1);
        let cast48_out1 = reshape30_out1;
        let cast49_out1 = reshape31_out1;
        let mul36_out1 = squeeze11_out1.clone().mul(cast48_out1);
        let shape46_out1: [i64; 4] = {
            let axes = &squeeze11_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather35_out1 = shape46_out1[3] as i64;
        let constant737_out1 = 2i64;
        let div16_out1 = gather35_out1 / constant737_out1;
        let cast50_out1 = div16_out1;
        let cast51_out1 = cast50_out1;
        let unsqueeze98_out1 = [cast51_out1 as i64];
        let slice38_out1 = squeeze11_out1
            .clone()
            .slice(s![.., .., .., unsqueeze98_out1[0]..9223372036854775807]);
        let neg8_out1 = slice38_out1.neg();
        let unsqueeze99_out1 = [cast51_out1 as i64];
        let slice39_out1 = squeeze11_out1.slice(s![.., .., .., 0..unsqueeze99_out1[0]]);
        let concat42_out1 = burn::tensor::Tensor::cat(
            [neg8_out1, slice39_out1].into(),
            3,
        );
        let mul37_out1 = concat42_out1.mul(cast49_out1);
        let add33_out1 = mul36_out1.add(mul37_out1);
        let shape47_out1: [i64; 4] = {
            let axes = &add32_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let slice40_out1: [i64; 1] = shape47_out1[3..4].try_into().unwrap();
        let cast52_out1 = {
            let shape_array = slice40_out1 as [i64; 1usize];
            let float_array: [f64; 1usize] = shape_array.map(|x| x as f64);
            Tensor::<
                1,
            >::from_data(
                TensorData::from(float_array),
                (&self.device, burn::tensor::DType::F32),
            )
        };
        let sqrt10_out1 = cast52_out1.sqrt();
        let constant748_out1 = self.constant748.val();
        let div17_out1 = constant748_out1.div(sqrt10_out1);
        let cast53_out1 = div17_out1;
        let transpose11_out1 = add33_out1.permute([0, 1, 3, 2]);
        let sqrt11_out1 = cast53_out1.clone().sqrt();
        let mul38_out1 = add32_out1
            .mul((sqrt11_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let sqrt12_out1 = cast53_out1.sqrt();
        let mul39_out1 = transpose11_out1
            .mul((sqrt12_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let matmul21_out1 = mul38_out1.matmul(mul39_out1);
        let constant750_out1 = self.constant750.val();
        let constant749_out1 = self.constant749.val();
        let where13_out1 = constant750_out1
            .unsqueeze_dims(&[0isize, 1isize, 2isize])
            .mask_where(
                less4_out1,
                constant749_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let add34_out1 = matmul21_out1.add(where13_out1);
        let softmax4_out1 = burn::tensor::activation::softmax(add34_out1, 3);
        let isnan4_out1 = softmax4_out1.clone().is_nan();
        let constant751_out1 = self.constant751.val();
        let where14_out1 = softmax4_out1
            .mask_where(
                isnan4_out1,
                constant751_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let matmul22_out1 = where14_out1.matmul(squeeze12_out1);
        let transpose12_out1 = matmul22_out1.permute([0, 2, 1, 3]);
        let unsqueeze100_out1 = [gather30_out1 as i64];
        let unsqueeze101_out1 = [gather31_out1 as i64];
        let constant754_out1: [i64; 1] = [512i64];
        let concat43_out1: [i64; 3usize] = [
            &unsqueeze100_out1[..],
            &unsqueeze101_out1[..],
            &constant754_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape34_out1 = transpose12_out1.reshape(concat43_out1);
        let linear15_out1 = self.linear15.forward(reshape34_out1);
        let add35_out1 = add31_out1.add(linear15_out1);
        add35_out1
    }
}
#[derive(Module, Debug)]
pub struct Submodule6 {
    layernormalization9: LayerNorm,
    linear16: Linear,
    constant755: burn::module::Param<Tensor<1>>,
    constant756: burn::module::Param<Tensor<1>>,
    constant757: burn::module::Param<Tensor<1>>,
    linear17: Linear,
    layernormalization10: LayerNorm,
    linear18: Linear,
    constant825: burn::module::Param<Tensor<1>>,
    constant827: burn::module::Param<Tensor<1>>,
    constant826: burn::module::Param<Tensor<1>>,
    constant828: burn::module::Param<Tensor<1>>,
    linear19: Linear,
    #[module(skip)]
    device: Device,
}
impl Submodule6 {
    #[allow(unused_variables)]
    pub fn new(device: &Device) -> Self {
        let layernormalization9 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear16 = LinearConfig::new(512, 2048).with_bias(true).init(device);
        let constant755: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1.4142135381698608f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant756: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant757: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([0.5f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let linear17 = LinearConfig::new(2048, 512).with_bias(true).init(device);
        let layernormalization10 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear18 = LinearConfig::new(512, 1536).with_bias(false).init(device);
        let constant825: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant827: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant826: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant828: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let linear19 = LinearConfig::new(512, 512).with_bias(true).init(device);
        Self {
            layernormalization9,
            linear16,
            constant755,
            constant756,
            constant757,
            linear17,
            layernormalization10,
            linear18,
            constant825,
            constant827,
            constant826,
            constant828,
            linear19,
            device: device.clone(),
        }
    }
    #[allow(clippy::let_and_return, clippy::approx_constant)]
    pub fn forward(
        &self,
        add35_out1: Tensor<3>,
        constant461_out1: Tensor<2>,
        constant474_out1: Tensor<2>,
        less4_out1: Tensor<4, Bool>,
    ) -> Tensor<3> {
        let layernormalization9_out1 = {
            let dtype = add35_out1.clone().dtype();
            self.layernormalization9
                .forward(add35_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let linear16_out1 = self.linear16.forward(layernormalization9_out1);
        let constant755_out1 = self.constant755.val();
        let div18_out1 = linear16_out1
            .clone()
            .div((constant755_out1).unsqueeze_dims(&[0isize, 1isize]));
        let erf4_out1 = div18_out1.erf();
        let constant756_out1 = self.constant756.val();
        let add36_out1 = erf4_out1
            .add((constant756_out1).unsqueeze_dims(&[0isize, 1isize]));
        let mul40_out1 = linear16_out1.mul(add36_out1);
        let constant757_out1 = self.constant757.val();
        let mul41_out1 = mul40_out1
            .mul((constant757_out1).unsqueeze_dims(&[0isize, 1isize]));
        let linear17_out1 = self.linear17.forward(mul41_out1);
        let add37_out1 = add35_out1.add(linear17_out1);
        let layernormalization10_out1 = {
            let dtype = add37_out1.clone().dtype();
            self.layernormalization10
                .forward(add37_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let shape48_out1: [i64; 3] = {
            let axes = &layernormalization10_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather36_out1 = shape48_out1[0] as i64;
        let shape49_out1: [i64; 3] = {
            let axes = &layernormalization10_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather37_out1 = shape49_out1[1] as i64;
        let linear18_out1 = self.linear18.forward(layernormalization10_out1);
        let unsqueeze102_out1 = [gather36_out1 as i64];
        let unsqueeze103_out1 = [gather37_out1 as i64];
        let constant764_out1: [i64; 1] = [64i64];
        let constant762_out1: [i64; 1] = [3i64];
        let constant763_out1: [i64; 1] = [8i64];
        let concat44_out1: [i64; 5usize] = [
            &unsqueeze102_out1[..],
            &unsqueeze103_out1[..],
            &constant762_out1[..],
            &constant763_out1[..],
            &constant764_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape35_out1 = linear18_out1.reshape(concat44_out1);
        let transpose13_out1 = reshape35_out1.permute([2, 0, 3, 1, 4]);
        let split_tensors = transpose13_out1.split_with_sizes([1, 1, 1].into(), 0);
        let [split5_out1, split5_out2, split5_out3] = split_tensors.try_into().unwrap();
        let squeeze13_out1 = split5_out1.squeeze_dims::<4>(&[0]);
        let squeeze14_out1 = split5_out2.squeeze_dims::<4>(&[0]);
        let squeeze15_out1 = split5_out3.squeeze_dims::<4>(&[0]);
        let shape50_out1: [i64; 4] = {
            let axes = &squeeze13_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather38_out1 = shape50_out1[2] as i64;
        let shape51_out1: [i64; 4] = {
            let axes = &squeeze14_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather39_out1 = shape51_out1[2] as i64;
        let sub7_out1 = gather39_out1 - gather38_out1;
        let unsqueeze104_out1 = [gather39_out1 as i64];
        let slice41_out1 = constant461_out1
            .clone()
            .slice(s![0..unsqueeze104_out1[0], ..]);
        let unsqueeze105_out1 = [gather39_out1 as i64];
        let constant778_out1: [i64; 1] = [64i64];
        let constant775_out1: [i64; 1] = [1i64];
        let constant776_out1: [i64; 1] = [1i64];
        let concat45_out1: [i64; 4usize] = [
            &constant775_out1[..],
            &constant776_out1[..],
            &unsqueeze105_out1[..],
            &constant778_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze106_out1 = [gather39_out1 as i64];
        let constant782_out1: [i64; 1] = [64i64];
        let constant779_out1: [i64; 1] = [1i64];
        let constant780_out1: [i64; 1] = [1i64];
        let concat46_out1: [i64; 4usize] = [
            &constant779_out1[..],
            &constant780_out1[..],
            &unsqueeze106_out1[..],
            &constant782_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape36_out1 = slice41_out1.reshape(concat45_out1);
        let unsqueeze107_out1 = [gather39_out1 as i64];
        let slice42_out1 = constant474_out1
            .clone()
            .slice(s![0..unsqueeze107_out1[0], ..]);
        let reshape37_out1 = slice42_out1.reshape(concat46_out1);
        let unsqueeze108_out1 = [sub7_out1 as i64];
        let unsqueeze109_out1 = [gather39_out1 as i64];
        let slice43_out1 = constant461_out1
            .slice(s![unsqueeze108_out1[0]..unsqueeze109_out1[0], ..]);
        let unsqueeze110_out1 = [gather38_out1 as i64];
        let constant794_out1: [i64; 1] = [64i64];
        let constant791_out1: [i64; 1] = [1i64];
        let constant792_out1: [i64; 1] = [1i64];
        let concat47_out1: [i64; 4usize] = [
            &constant791_out1[..],
            &constant792_out1[..],
            &unsqueeze110_out1[..],
            &constant794_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze111_out1 = [gather38_out1 as i64];
        let constant798_out1: [i64; 1] = [64i64];
        let constant795_out1: [i64; 1] = [1i64];
        let constant796_out1: [i64; 1] = [1i64];
        let concat48_out1: [i64; 4usize] = [
            &constant795_out1[..],
            &constant796_out1[..],
            &unsqueeze111_out1[..],
            &constant798_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape38_out1 = slice43_out1.reshape(concat47_out1);
        let unsqueeze112_out1 = [sub7_out1 as i64];
        let unsqueeze113_out1 = [gather39_out1 as i64];
        let slice44_out1 = constant474_out1
            .slice(s![unsqueeze112_out1[0]..unsqueeze113_out1[0], ..]);
        let reshape39_out1 = slice44_out1.reshape(concat48_out1);
        let cast54_out1 = reshape38_out1;
        let cast55_out1 = reshape39_out1;
        let mul42_out1 = squeeze13_out1.clone().mul(cast54_out1);
        let shape52_out1: [i64; 4] = {
            let axes = &squeeze13_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather40_out1 = shape52_out1[3] as i64;
        let constant804_out1 = 2i64;
        let div19_out1 = gather40_out1 / constant804_out1;
        let cast56_out1 = div19_out1;
        let cast57_out1 = cast56_out1;
        let unsqueeze114_out1 = [cast57_out1 as i64];
        let slice45_out1 = squeeze13_out1
            .clone()
            .slice(s![.., .., .., unsqueeze114_out1[0]..9223372036854775807]);
        let neg9_out1 = slice45_out1.neg();
        let unsqueeze115_out1 = [cast57_out1 as i64];
        let slice46_out1 = squeeze13_out1.slice(s![.., .., .., 0..unsqueeze115_out1[0]]);
        let concat49_out1 = burn::tensor::Tensor::cat(
            [neg9_out1, slice46_out1].into(),
            3,
        );
        let mul43_out1 = concat49_out1.mul(cast55_out1);
        let add38_out1 = mul42_out1.add(mul43_out1);
        let cast58_out1 = reshape36_out1;
        let cast59_out1 = reshape37_out1;
        let mul44_out1 = squeeze14_out1.clone().mul(cast58_out1);
        let shape53_out1: [i64; 4] = {
            let axes = &squeeze14_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather41_out1 = shape53_out1[3] as i64;
        let constant814_out1 = 2i64;
        let div20_out1 = gather41_out1 / constant814_out1;
        let cast60_out1 = div20_out1;
        let cast61_out1 = cast60_out1;
        let unsqueeze116_out1 = [cast61_out1 as i64];
        let slice47_out1 = squeeze14_out1
            .clone()
            .slice(s![.., .., .., unsqueeze116_out1[0]..9223372036854775807]);
        let neg10_out1 = slice47_out1.neg();
        let unsqueeze117_out1 = [cast61_out1 as i64];
        let slice48_out1 = squeeze14_out1.slice(s![.., .., .., 0..unsqueeze117_out1[0]]);
        let concat50_out1 = burn::tensor::Tensor::cat(
            [neg10_out1, slice48_out1].into(),
            3,
        );
        let mul45_out1 = concat50_out1.mul(cast59_out1);
        let add39_out1 = mul44_out1.add(mul45_out1);
        let shape54_out1: [i64; 4] = {
            let axes = &add38_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let slice49_out1: [i64; 1] = shape54_out1[3..4].try_into().unwrap();
        let cast62_out1 = {
            let shape_array = slice49_out1 as [i64; 1usize];
            let float_array: [f64; 1usize] = shape_array.map(|x| x as f64);
            Tensor::<
                1,
            >::from_data(
                TensorData::from(float_array),
                (&self.device, burn::tensor::DType::F32),
            )
        };
        let sqrt13_out1 = cast62_out1.sqrt();
        let constant825_out1 = self.constant825.val();
        let div21_out1 = constant825_out1.div(sqrt13_out1);
        let cast63_out1 = div21_out1;
        let transpose14_out1 = add39_out1.permute([0, 1, 3, 2]);
        let sqrt14_out1 = cast63_out1.clone().sqrt();
        let mul46_out1 = add38_out1
            .mul((sqrt14_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let sqrt15_out1 = cast63_out1.sqrt();
        let mul47_out1 = transpose14_out1
            .mul((sqrt15_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let matmul27_out1 = mul46_out1.matmul(mul47_out1);
        let constant827_out1 = self.constant827.val();
        let constant826_out1 = self.constant826.val();
        let where15_out1 = constant827_out1
            .unsqueeze_dims(&[0isize, 1isize, 2isize])
            .mask_where(
                less4_out1,
                constant826_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let add40_out1 = matmul27_out1.add(where15_out1);
        let softmax5_out1 = burn::tensor::activation::softmax(add40_out1, 3);
        let isnan5_out1 = softmax5_out1.clone().is_nan();
        let constant828_out1 = self.constant828.val();
        let where16_out1 = softmax5_out1
            .mask_where(
                isnan5_out1,
                constant828_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let matmul28_out1 = where16_out1.matmul(squeeze15_out1);
        let transpose15_out1 = matmul28_out1.permute([0, 2, 1, 3]);
        let unsqueeze118_out1 = [gather36_out1 as i64];
        let unsqueeze119_out1 = [gather37_out1 as i64];
        let constant831_out1: [i64; 1] = [512i64];
        let concat51_out1: [i64; 3usize] = [
            &unsqueeze118_out1[..],
            &unsqueeze119_out1[..],
            &constant831_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape40_out1 = transpose15_out1.reshape(concat51_out1);
        let linear19_out1 = self.linear19.forward(reshape40_out1);
        let add41_out1 = add37_out1.add(linear19_out1);
        add41_out1
    }
}
#[derive(Module, Debug)]
pub struct Submodule7 {
    layernormalization11: LayerNorm,
    linear20: Linear,
    constant832: burn::module::Param<Tensor<1>>,
    constant833: burn::module::Param<Tensor<1>>,
    constant834: burn::module::Param<Tensor<1>>,
    linear21: Linear,
    layernormalization12: LayerNorm,
    linear22: Linear,
    constant902: burn::module::Param<Tensor<1>>,
    constant904: burn::module::Param<Tensor<1>>,
    constant903: burn::module::Param<Tensor<1>>,
    constant905: burn::module::Param<Tensor<1>>,
    linear23: Linear,
    #[module(skip)]
    device: Device,
}
impl Submodule7 {
    #[allow(unused_variables)]
    pub fn new(device: &Device) -> Self {
        let layernormalization11 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear20 = LinearConfig::new(512, 2048).with_bias(true).init(device);
        let constant832: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1.4142135381698608f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant833: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant834: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([0.5f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let linear21 = LinearConfig::new(2048, 512).with_bias(true).init(device);
        let layernormalization12 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear22 = LinearConfig::new(512, 1536).with_bias(false).init(device);
        let constant902: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant904: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant903: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant905: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let linear23 = LinearConfig::new(512, 512).with_bias(true).init(device);
        Self {
            layernormalization11,
            linear20,
            constant832,
            constant833,
            constant834,
            linear21,
            layernormalization12,
            linear22,
            constant902,
            constant904,
            constant903,
            constant905,
            linear23,
            device: device.clone(),
        }
    }
    #[allow(clippy::let_and_return, clippy::approx_constant)]
    pub fn forward(
        &self,
        add41_out1: Tensor<3>,
        constant461_out1: Tensor<2>,
        constant474_out1: Tensor<2>,
        less4_out1: Tensor<4, Bool>,
    ) -> Tensor<3> {
        let layernormalization11_out1 = {
            let dtype = add41_out1.clone().dtype();
            self.layernormalization11
                .forward(add41_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let linear20_out1 = self.linear20.forward(layernormalization11_out1);
        let constant832_out1 = self.constant832.val();
        let div22_out1 = linear20_out1
            .clone()
            .div((constant832_out1).unsqueeze_dims(&[0isize, 1isize]));
        let erf5_out1 = div22_out1.erf();
        let constant833_out1 = self.constant833.val();
        let add42_out1 = erf5_out1
            .add((constant833_out1).unsqueeze_dims(&[0isize, 1isize]));
        let mul48_out1 = linear20_out1.mul(add42_out1);
        let constant834_out1 = self.constant834.val();
        let mul49_out1 = mul48_out1
            .mul((constant834_out1).unsqueeze_dims(&[0isize, 1isize]));
        let linear21_out1 = self.linear21.forward(mul49_out1);
        let add43_out1 = add41_out1.add(linear21_out1);
        let layernormalization12_out1 = {
            let dtype = add43_out1.clone().dtype();
            self.layernormalization12
                .forward(add43_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let shape55_out1: [i64; 3] = {
            let axes = &layernormalization12_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather42_out1 = shape55_out1[0] as i64;
        let shape56_out1: [i64; 3] = {
            let axes = &layernormalization12_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather43_out1 = shape56_out1[1] as i64;
        let linear22_out1 = self.linear22.forward(layernormalization12_out1);
        let unsqueeze120_out1 = [gather42_out1 as i64];
        let unsqueeze121_out1 = [gather43_out1 as i64];
        let constant841_out1: [i64; 1] = [64i64];
        let constant839_out1: [i64; 1] = [3i64];
        let constant840_out1: [i64; 1] = [8i64];
        let concat52_out1: [i64; 5usize] = [
            &unsqueeze120_out1[..],
            &unsqueeze121_out1[..],
            &constant839_out1[..],
            &constant840_out1[..],
            &constant841_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape41_out1 = linear22_out1.reshape(concat52_out1);
        let transpose16_out1 = reshape41_out1.permute([2, 0, 3, 1, 4]);
        let split_tensors = transpose16_out1.split_with_sizes([1, 1, 1].into(), 0);
        let [split6_out1, split6_out2, split6_out3] = split_tensors.try_into().unwrap();
        let squeeze16_out1 = split6_out1.squeeze_dims::<4>(&[0]);
        let squeeze17_out1 = split6_out2.squeeze_dims::<4>(&[0]);
        let squeeze18_out1 = split6_out3.squeeze_dims::<4>(&[0]);
        let shape57_out1: [i64; 4] = {
            let axes = &squeeze16_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather44_out1 = shape57_out1[2] as i64;
        let shape58_out1: [i64; 4] = {
            let axes = &squeeze17_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather45_out1 = shape58_out1[2] as i64;
        let sub8_out1 = gather45_out1 - gather44_out1;
        let unsqueeze122_out1 = [gather45_out1 as i64];
        let slice50_out1 = constant461_out1
            .clone()
            .slice(s![0..unsqueeze122_out1[0], ..]);
        let unsqueeze123_out1 = [gather45_out1 as i64];
        let constant855_out1: [i64; 1] = [64i64];
        let constant852_out1: [i64; 1] = [1i64];
        let constant853_out1: [i64; 1] = [1i64];
        let concat53_out1: [i64; 4usize] = [
            &constant852_out1[..],
            &constant853_out1[..],
            &unsqueeze123_out1[..],
            &constant855_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze124_out1 = [gather45_out1 as i64];
        let constant859_out1: [i64; 1] = [64i64];
        let constant856_out1: [i64; 1] = [1i64];
        let constant857_out1: [i64; 1] = [1i64];
        let concat54_out1: [i64; 4usize] = [
            &constant856_out1[..],
            &constant857_out1[..],
            &unsqueeze124_out1[..],
            &constant859_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape42_out1 = slice50_out1.reshape(concat53_out1);
        let unsqueeze125_out1 = [gather45_out1 as i64];
        let slice51_out1 = constant474_out1
            .clone()
            .slice(s![0..unsqueeze125_out1[0], ..]);
        let reshape43_out1 = slice51_out1.reshape(concat54_out1);
        let unsqueeze126_out1 = [sub8_out1 as i64];
        let unsqueeze127_out1 = [gather45_out1 as i64];
        let slice52_out1 = constant461_out1
            .slice(s![unsqueeze126_out1[0]..unsqueeze127_out1[0], ..]);
        let unsqueeze128_out1 = [gather44_out1 as i64];
        let constant871_out1: [i64; 1] = [64i64];
        let constant868_out1: [i64; 1] = [1i64];
        let constant869_out1: [i64; 1] = [1i64];
        let concat55_out1: [i64; 4usize] = [
            &constant868_out1[..],
            &constant869_out1[..],
            &unsqueeze128_out1[..],
            &constant871_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze129_out1 = [gather44_out1 as i64];
        let constant875_out1: [i64; 1] = [64i64];
        let constant872_out1: [i64; 1] = [1i64];
        let constant873_out1: [i64; 1] = [1i64];
        let concat56_out1: [i64; 4usize] = [
            &constant872_out1[..],
            &constant873_out1[..],
            &unsqueeze129_out1[..],
            &constant875_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape44_out1 = slice52_out1.reshape(concat55_out1);
        let unsqueeze130_out1 = [sub8_out1 as i64];
        let unsqueeze131_out1 = [gather45_out1 as i64];
        let slice53_out1 = constant474_out1
            .slice(s![unsqueeze130_out1[0]..unsqueeze131_out1[0], ..]);
        let reshape45_out1 = slice53_out1.reshape(concat56_out1);
        let cast64_out1 = reshape44_out1;
        let cast65_out1 = reshape45_out1;
        let mul50_out1 = squeeze16_out1.clone().mul(cast64_out1);
        let shape59_out1: [i64; 4] = {
            let axes = &squeeze16_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather46_out1 = shape59_out1[3] as i64;
        let constant881_out1 = 2i64;
        let div23_out1 = gather46_out1 / constant881_out1;
        let cast66_out1 = div23_out1;
        let cast67_out1 = cast66_out1;
        let unsqueeze132_out1 = [cast67_out1 as i64];
        let slice54_out1 = squeeze16_out1
            .clone()
            .slice(s![.., .., .., unsqueeze132_out1[0]..9223372036854775807]);
        let neg11_out1 = slice54_out1.neg();
        let unsqueeze133_out1 = [cast67_out1 as i64];
        let slice55_out1 = squeeze16_out1.slice(s![.., .., .., 0..unsqueeze133_out1[0]]);
        let concat57_out1 = burn::tensor::Tensor::cat(
            [neg11_out1, slice55_out1].into(),
            3,
        );
        let mul51_out1 = concat57_out1.mul(cast65_out1);
        let add44_out1 = mul50_out1.add(mul51_out1);
        let cast68_out1 = reshape42_out1;
        let cast69_out1 = reshape43_out1;
        let mul52_out1 = squeeze17_out1.clone().mul(cast68_out1);
        let shape60_out1: [i64; 4] = {
            let axes = &squeeze17_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather47_out1 = shape60_out1[3] as i64;
        let constant891_out1 = 2i64;
        let div24_out1 = gather47_out1 / constant891_out1;
        let cast70_out1 = div24_out1;
        let cast71_out1 = cast70_out1;
        let unsqueeze134_out1 = [cast71_out1 as i64];
        let slice56_out1 = squeeze17_out1
            .clone()
            .slice(s![.., .., .., unsqueeze134_out1[0]..9223372036854775807]);
        let neg12_out1 = slice56_out1.neg();
        let unsqueeze135_out1 = [cast71_out1 as i64];
        let slice57_out1 = squeeze17_out1.slice(s![.., .., .., 0..unsqueeze135_out1[0]]);
        let concat58_out1 = burn::tensor::Tensor::cat(
            [neg12_out1, slice57_out1].into(),
            3,
        );
        let mul53_out1 = concat58_out1.mul(cast69_out1);
        let add45_out1 = mul52_out1.add(mul53_out1);
        let shape61_out1: [i64; 4] = {
            let axes = &add44_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let slice58_out1: [i64; 1] = shape61_out1[3..4].try_into().unwrap();
        let cast72_out1 = {
            let shape_array = slice58_out1 as [i64; 1usize];
            let float_array: [f64; 1usize] = shape_array.map(|x| x as f64);
            Tensor::<
                1,
            >::from_data(
                TensorData::from(float_array),
                (&self.device, burn::tensor::DType::F32),
            )
        };
        let sqrt16_out1 = cast72_out1.sqrt();
        let constant902_out1 = self.constant902.val();
        let div25_out1 = constant902_out1.div(sqrt16_out1);
        let cast73_out1 = div25_out1;
        let transpose17_out1 = add45_out1.permute([0, 1, 3, 2]);
        let sqrt17_out1 = cast73_out1.clone().sqrt();
        let mul54_out1 = add44_out1
            .mul((sqrt17_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let sqrt18_out1 = cast73_out1.sqrt();
        let mul55_out1 = transpose17_out1
            .mul((sqrt18_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let matmul33_out1 = mul54_out1.matmul(mul55_out1);
        let constant904_out1 = self.constant904.val();
        let constant903_out1 = self.constant903.val();
        let where17_out1 = constant904_out1
            .unsqueeze_dims(&[0isize, 1isize, 2isize])
            .mask_where(
                less4_out1,
                constant903_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let add46_out1 = matmul33_out1.add(where17_out1);
        let softmax6_out1 = burn::tensor::activation::softmax(add46_out1, 3);
        let isnan6_out1 = softmax6_out1.clone().is_nan();
        let constant905_out1 = self.constant905.val();
        let where18_out1 = softmax6_out1
            .mask_where(
                isnan6_out1,
                constant905_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let matmul34_out1 = where18_out1.matmul(squeeze18_out1);
        let transpose18_out1 = matmul34_out1.permute([0, 2, 1, 3]);
        let unsqueeze136_out1 = [gather42_out1 as i64];
        let unsqueeze137_out1 = [gather43_out1 as i64];
        let constant908_out1: [i64; 1] = [512i64];
        let concat59_out1: [i64; 3usize] = [
            &unsqueeze136_out1[..],
            &unsqueeze137_out1[..],
            &constant908_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape46_out1 = transpose18_out1.reshape(concat59_out1);
        let linear23_out1 = self.linear23.forward(reshape46_out1);
        let add47_out1 = add43_out1.add(linear23_out1);
        add47_out1
    }
}
#[derive(Module, Debug)]
pub struct Submodule8 {
    layernormalization13: LayerNorm,
    linear24: Linear,
    constant909: burn::module::Param<Tensor<1>>,
    constant910: burn::module::Param<Tensor<1>>,
    constant911: burn::module::Param<Tensor<1>>,
    linear25: Linear,
    layernormalization14: LayerNorm,
    linear26: Linear,
    constant979: burn::module::Param<Tensor<1>>,
    constant981: burn::module::Param<Tensor<1>>,
    constant980: burn::module::Param<Tensor<1>>,
    constant982: burn::module::Param<Tensor<1>>,
    linear27: Linear,
    #[module(skip)]
    device: Device,
}
impl Submodule8 {
    #[allow(unused_variables)]
    pub fn new(device: &Device) -> Self {
        let layernormalization13 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear24 = LinearConfig::new(512, 2048).with_bias(true).init(device);
        let constant909: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1.4142135381698608f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant910: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant911: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([0.5f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let linear25 = LinearConfig::new(2048, 512).with_bias(true).init(device);
        let layernormalization14 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear26 = LinearConfig::new(512, 1536).with_bias(false).init(device);
        let constant979: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant981: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant980: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant982: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let linear27 = LinearConfig::new(512, 512).with_bias(true).init(device);
        Self {
            layernormalization13,
            linear24,
            constant909,
            constant910,
            constant911,
            linear25,
            layernormalization14,
            linear26,
            constant979,
            constant981,
            constant980,
            constant982,
            linear27,
            device: device.clone(),
        }
    }
    #[allow(clippy::let_and_return, clippy::approx_constant)]
    pub fn forward(
        &self,
        add47_out1: Tensor<3>,
        constant461_out1: Tensor<2>,
        constant474_out1: Tensor<2>,
        less4_out1: Tensor<4, Bool>,
    ) -> Tensor<3> {
        let layernormalization13_out1 = {
            let dtype = add47_out1.clone().dtype();
            self.layernormalization13
                .forward(add47_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let linear24_out1 = self.linear24.forward(layernormalization13_out1);
        let constant909_out1 = self.constant909.val();
        let div26_out1 = linear24_out1
            .clone()
            .div((constant909_out1).unsqueeze_dims(&[0isize, 1isize]));
        let erf6_out1 = div26_out1.erf();
        let constant910_out1 = self.constant910.val();
        let add48_out1 = erf6_out1
            .add((constant910_out1).unsqueeze_dims(&[0isize, 1isize]));
        let mul56_out1 = linear24_out1.mul(add48_out1);
        let constant911_out1 = self.constant911.val();
        let mul57_out1 = mul56_out1
            .mul((constant911_out1).unsqueeze_dims(&[0isize, 1isize]));
        let linear25_out1 = self.linear25.forward(mul57_out1);
        let add49_out1 = add47_out1.add(linear25_out1);
        let layernormalization14_out1 = {
            let dtype = add49_out1.clone().dtype();
            self.layernormalization14
                .forward(add49_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let shape62_out1: [i64; 3] = {
            let axes = &layernormalization14_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather48_out1 = shape62_out1[0] as i64;
        let shape63_out1: [i64; 3] = {
            let axes = &layernormalization14_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather49_out1 = shape63_out1[1] as i64;
        let linear26_out1 = self.linear26.forward(layernormalization14_out1);
        let unsqueeze138_out1 = [gather48_out1 as i64];
        let unsqueeze139_out1 = [gather49_out1 as i64];
        let constant918_out1: [i64; 1] = [64i64];
        let constant916_out1: [i64; 1] = [3i64];
        let constant917_out1: [i64; 1] = [8i64];
        let concat60_out1: [i64; 5usize] = [
            &unsqueeze138_out1[..],
            &unsqueeze139_out1[..],
            &constant916_out1[..],
            &constant917_out1[..],
            &constant918_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape47_out1 = linear26_out1.reshape(concat60_out1);
        let transpose19_out1 = reshape47_out1.permute([2, 0, 3, 1, 4]);
        let split_tensors = transpose19_out1.split_with_sizes([1, 1, 1].into(), 0);
        let [split7_out1, split7_out2, split7_out3] = split_tensors.try_into().unwrap();
        let squeeze19_out1 = split7_out1.squeeze_dims::<4>(&[0]);
        let squeeze20_out1 = split7_out2.squeeze_dims::<4>(&[0]);
        let squeeze21_out1 = split7_out3.squeeze_dims::<4>(&[0]);
        let shape64_out1: [i64; 4] = {
            let axes = &squeeze19_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather50_out1 = shape64_out1[2] as i64;
        let shape65_out1: [i64; 4] = {
            let axes = &squeeze20_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather51_out1 = shape65_out1[2] as i64;
        let sub9_out1 = gather51_out1 - gather50_out1;
        let unsqueeze140_out1 = [gather51_out1 as i64];
        let slice59_out1 = constant461_out1
            .clone()
            .slice(s![0..unsqueeze140_out1[0], ..]);
        let unsqueeze141_out1 = [gather51_out1 as i64];
        let constant932_out1: [i64; 1] = [64i64];
        let constant929_out1: [i64; 1] = [1i64];
        let constant930_out1: [i64; 1] = [1i64];
        let concat61_out1: [i64; 4usize] = [
            &constant929_out1[..],
            &constant930_out1[..],
            &unsqueeze141_out1[..],
            &constant932_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze142_out1 = [gather51_out1 as i64];
        let constant936_out1: [i64; 1] = [64i64];
        let constant933_out1: [i64; 1] = [1i64];
        let constant934_out1: [i64; 1] = [1i64];
        let concat62_out1: [i64; 4usize] = [
            &constant933_out1[..],
            &constant934_out1[..],
            &unsqueeze142_out1[..],
            &constant936_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape48_out1 = slice59_out1.reshape(concat61_out1);
        let unsqueeze143_out1 = [gather51_out1 as i64];
        let slice60_out1 = constant474_out1
            .clone()
            .slice(s![0..unsqueeze143_out1[0], ..]);
        let reshape49_out1 = slice60_out1.reshape(concat62_out1);
        let unsqueeze144_out1 = [sub9_out1 as i64];
        let unsqueeze145_out1 = [gather51_out1 as i64];
        let slice61_out1 = constant461_out1
            .slice(s![unsqueeze144_out1[0]..unsqueeze145_out1[0], ..]);
        let unsqueeze146_out1 = [gather50_out1 as i64];
        let constant948_out1: [i64; 1] = [64i64];
        let constant945_out1: [i64; 1] = [1i64];
        let constant946_out1: [i64; 1] = [1i64];
        let concat63_out1: [i64; 4usize] = [
            &constant945_out1[..],
            &constant946_out1[..],
            &unsqueeze146_out1[..],
            &constant948_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze147_out1 = [gather50_out1 as i64];
        let constant952_out1: [i64; 1] = [64i64];
        let constant949_out1: [i64; 1] = [1i64];
        let constant950_out1: [i64; 1] = [1i64];
        let concat64_out1: [i64; 4usize] = [
            &constant949_out1[..],
            &constant950_out1[..],
            &unsqueeze147_out1[..],
            &constant952_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape50_out1 = slice61_out1.reshape(concat63_out1);
        let unsqueeze148_out1 = [sub9_out1 as i64];
        let unsqueeze149_out1 = [gather51_out1 as i64];
        let slice62_out1 = constant474_out1
            .slice(s![unsqueeze148_out1[0]..unsqueeze149_out1[0], ..]);
        let reshape51_out1 = slice62_out1.reshape(concat64_out1);
        let cast74_out1 = reshape50_out1;
        let cast75_out1 = reshape51_out1;
        let mul58_out1 = squeeze19_out1.clone().mul(cast74_out1);
        let shape66_out1: [i64; 4] = {
            let axes = &squeeze19_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather52_out1 = shape66_out1[3] as i64;
        let constant958_out1 = 2i64;
        let div27_out1 = gather52_out1 / constant958_out1;
        let cast76_out1 = div27_out1;
        let cast77_out1 = cast76_out1;
        let unsqueeze150_out1 = [cast77_out1 as i64];
        let slice63_out1 = squeeze19_out1
            .clone()
            .slice(s![.., .., .., unsqueeze150_out1[0]..9223372036854775807]);
        let neg13_out1 = slice63_out1.neg();
        let unsqueeze151_out1 = [cast77_out1 as i64];
        let slice64_out1 = squeeze19_out1.slice(s![.., .., .., 0..unsqueeze151_out1[0]]);
        let concat65_out1 = burn::tensor::Tensor::cat(
            [neg13_out1, slice64_out1].into(),
            3,
        );
        let mul59_out1 = concat65_out1.mul(cast75_out1);
        let add50_out1 = mul58_out1.add(mul59_out1);
        let cast78_out1 = reshape48_out1;
        let cast79_out1 = reshape49_out1;
        let mul60_out1 = squeeze20_out1.clone().mul(cast78_out1);
        let shape67_out1: [i64; 4] = {
            let axes = &squeeze20_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather53_out1 = shape67_out1[3] as i64;
        let constant968_out1 = 2i64;
        let div28_out1 = gather53_out1 / constant968_out1;
        let cast80_out1 = div28_out1;
        let cast81_out1 = cast80_out1;
        let unsqueeze152_out1 = [cast81_out1 as i64];
        let slice65_out1 = squeeze20_out1
            .clone()
            .slice(s![.., .., .., unsqueeze152_out1[0]..9223372036854775807]);
        let neg14_out1 = slice65_out1.neg();
        let unsqueeze153_out1 = [cast81_out1 as i64];
        let slice66_out1 = squeeze20_out1.slice(s![.., .., .., 0..unsqueeze153_out1[0]]);
        let concat66_out1 = burn::tensor::Tensor::cat(
            [neg14_out1, slice66_out1].into(),
            3,
        );
        let mul61_out1 = concat66_out1.mul(cast79_out1);
        let add51_out1 = mul60_out1.add(mul61_out1);
        let shape68_out1: [i64; 4] = {
            let axes = &add50_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let slice67_out1: [i64; 1] = shape68_out1[3..4].try_into().unwrap();
        let cast82_out1 = {
            let shape_array = slice67_out1 as [i64; 1usize];
            let float_array: [f64; 1usize] = shape_array.map(|x| x as f64);
            Tensor::<
                1,
            >::from_data(
                TensorData::from(float_array),
                (&self.device, burn::tensor::DType::F32),
            )
        };
        let sqrt19_out1 = cast82_out1.sqrt();
        let constant979_out1 = self.constant979.val();
        let div29_out1 = constant979_out1.div(sqrt19_out1);
        let cast83_out1 = div29_out1;
        let transpose20_out1 = add51_out1.permute([0, 1, 3, 2]);
        let sqrt20_out1 = cast83_out1.clone().sqrt();
        let mul62_out1 = add50_out1
            .mul((sqrt20_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let sqrt21_out1 = cast83_out1.sqrt();
        let mul63_out1 = transpose20_out1
            .mul((sqrt21_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let matmul39_out1 = mul62_out1.matmul(mul63_out1);
        let constant981_out1 = self.constant981.val();
        let constant980_out1 = self.constant980.val();
        let where19_out1 = constant981_out1
            .unsqueeze_dims(&[0isize, 1isize, 2isize])
            .mask_where(
                less4_out1,
                constant980_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let add52_out1 = matmul39_out1.add(where19_out1);
        let softmax7_out1 = burn::tensor::activation::softmax(add52_out1, 3);
        let isnan7_out1 = softmax7_out1.clone().is_nan();
        let constant982_out1 = self.constant982.val();
        let where20_out1 = softmax7_out1
            .mask_where(
                isnan7_out1,
                constant982_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let matmul40_out1 = where20_out1.matmul(squeeze21_out1);
        let transpose21_out1 = matmul40_out1.permute([0, 2, 1, 3]);
        let unsqueeze154_out1 = [gather48_out1 as i64];
        let unsqueeze155_out1 = [gather49_out1 as i64];
        let constant985_out1: [i64; 1] = [512i64];
        let concat67_out1: [i64; 3usize] = [
            &unsqueeze154_out1[..],
            &unsqueeze155_out1[..],
            &constant985_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape52_out1 = transpose21_out1.reshape(concat67_out1);
        let linear27_out1 = self.linear27.forward(reshape52_out1);
        let add53_out1 = add49_out1.add(linear27_out1);
        add53_out1
    }
}
#[derive(Module, Debug)]
pub struct Submodule9 {
    layernormalization15: LayerNorm,
    linear28: Linear,
    constant986: burn::module::Param<Tensor<1>>,
    constant987: burn::module::Param<Tensor<1>>,
    constant988: burn::module::Param<Tensor<1>>,
    linear29: Linear,
    layernormalization16: LayerNorm,
    linear30: Linear,
    constant1056: burn::module::Param<Tensor<1>>,
    constant1058: burn::module::Param<Tensor<1>>,
    constant1057: burn::module::Param<Tensor<1>>,
    constant1059: burn::module::Param<Tensor<1>>,
    linear31: Linear,
    #[module(skip)]
    device: Device,
}
impl Submodule9 {
    #[allow(unused_variables)]
    pub fn new(device: &Device) -> Self {
        let layernormalization15 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear28 = LinearConfig::new(512, 2048).with_bias(true).init(device);
        let constant986: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1.4142135381698608f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant987: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant988: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([0.5f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let linear29 = LinearConfig::new(2048, 512).with_bias(true).init(device);
        let layernormalization16 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear30 = LinearConfig::new(512, 1536).with_bias(false).init(device);
        let constant1056: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1058: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1057: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1059: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let linear31 = LinearConfig::new(512, 512).with_bias(true).init(device);
        Self {
            layernormalization15,
            linear28,
            constant986,
            constant987,
            constant988,
            linear29,
            layernormalization16,
            linear30,
            constant1056,
            constant1058,
            constant1057,
            constant1059,
            linear31,
            device: device.clone(),
        }
    }
    #[allow(clippy::let_and_return, clippy::approx_constant)]
    pub fn forward(
        &self,
        add53_out1: Tensor<3>,
        constant461_out1: Tensor<2>,
        constant474_out1: Tensor<2>,
        less4_out1: Tensor<4, Bool>,
    ) -> Tensor<3> {
        let layernormalization15_out1 = {
            let dtype = add53_out1.clone().dtype();
            self.layernormalization15
                .forward(add53_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let linear28_out1 = self.linear28.forward(layernormalization15_out1);
        let constant986_out1 = self.constant986.val();
        let div30_out1 = linear28_out1
            .clone()
            .div((constant986_out1).unsqueeze_dims(&[0isize, 1isize]));
        let erf7_out1 = div30_out1.erf();
        let constant987_out1 = self.constant987.val();
        let add54_out1 = erf7_out1
            .add((constant987_out1).unsqueeze_dims(&[0isize, 1isize]));
        let mul64_out1 = linear28_out1.mul(add54_out1);
        let constant988_out1 = self.constant988.val();
        let mul65_out1 = mul64_out1
            .mul((constant988_out1).unsqueeze_dims(&[0isize, 1isize]));
        let linear29_out1 = self.linear29.forward(mul65_out1);
        let add55_out1 = add53_out1.add(linear29_out1);
        let layernormalization16_out1 = {
            let dtype = add55_out1.clone().dtype();
            self.layernormalization16
                .forward(add55_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let shape69_out1: [i64; 3] = {
            let axes = &layernormalization16_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather54_out1 = shape69_out1[0] as i64;
        let shape70_out1: [i64; 3] = {
            let axes = &layernormalization16_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather55_out1 = shape70_out1[1] as i64;
        let linear30_out1 = self.linear30.forward(layernormalization16_out1);
        let unsqueeze156_out1 = [gather54_out1 as i64];
        let unsqueeze157_out1 = [gather55_out1 as i64];
        let constant995_out1: [i64; 1] = [64i64];
        let constant993_out1: [i64; 1] = [3i64];
        let constant994_out1: [i64; 1] = [8i64];
        let concat68_out1: [i64; 5usize] = [
            &unsqueeze156_out1[..],
            &unsqueeze157_out1[..],
            &constant993_out1[..],
            &constant994_out1[..],
            &constant995_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape53_out1 = linear30_out1.reshape(concat68_out1);
        let transpose22_out1 = reshape53_out1.permute([2, 0, 3, 1, 4]);
        let split_tensors = transpose22_out1.split_with_sizes([1, 1, 1].into(), 0);
        let [split8_out1, split8_out2, split8_out3] = split_tensors.try_into().unwrap();
        let squeeze22_out1 = split8_out1.squeeze_dims::<4>(&[0]);
        let squeeze23_out1 = split8_out2.squeeze_dims::<4>(&[0]);
        let squeeze24_out1 = split8_out3.squeeze_dims::<4>(&[0]);
        let shape71_out1: [i64; 4] = {
            let axes = &squeeze22_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather56_out1 = shape71_out1[2] as i64;
        let shape72_out1: [i64; 4] = {
            let axes = &squeeze23_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather57_out1 = shape72_out1[2] as i64;
        let sub10_out1 = gather57_out1 - gather56_out1;
        let unsqueeze158_out1 = [gather57_out1 as i64];
        let slice68_out1 = constant461_out1
            .clone()
            .slice(s![0..unsqueeze158_out1[0], ..]);
        let unsqueeze159_out1 = [gather57_out1 as i64];
        let constant1009_out1: [i64; 1] = [64i64];
        let constant1006_out1: [i64; 1] = [1i64];
        let constant1007_out1: [i64; 1] = [1i64];
        let concat69_out1: [i64; 4usize] = [
            &constant1006_out1[..],
            &constant1007_out1[..],
            &unsqueeze159_out1[..],
            &constant1009_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze160_out1 = [gather57_out1 as i64];
        let constant1013_out1: [i64; 1] = [64i64];
        let constant1010_out1: [i64; 1] = [1i64];
        let constant1011_out1: [i64; 1] = [1i64];
        let concat70_out1: [i64; 4usize] = [
            &constant1010_out1[..],
            &constant1011_out1[..],
            &unsqueeze160_out1[..],
            &constant1013_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape54_out1 = slice68_out1.reshape(concat69_out1);
        let unsqueeze161_out1 = [gather57_out1 as i64];
        let slice69_out1 = constant474_out1
            .clone()
            .slice(s![0..unsqueeze161_out1[0], ..]);
        let reshape55_out1 = slice69_out1.reshape(concat70_out1);
        let unsqueeze162_out1 = [sub10_out1 as i64];
        let unsqueeze163_out1 = [gather57_out1 as i64];
        let slice70_out1 = constant461_out1
            .slice(s![unsqueeze162_out1[0]..unsqueeze163_out1[0], ..]);
        let unsqueeze164_out1 = [gather56_out1 as i64];
        let constant1025_out1: [i64; 1] = [64i64];
        let constant1022_out1: [i64; 1] = [1i64];
        let constant1023_out1: [i64; 1] = [1i64];
        let concat71_out1: [i64; 4usize] = [
            &constant1022_out1[..],
            &constant1023_out1[..],
            &unsqueeze164_out1[..],
            &constant1025_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze165_out1 = [gather56_out1 as i64];
        let constant1029_out1: [i64; 1] = [64i64];
        let constant1026_out1: [i64; 1] = [1i64];
        let constant1027_out1: [i64; 1] = [1i64];
        let concat72_out1: [i64; 4usize] = [
            &constant1026_out1[..],
            &constant1027_out1[..],
            &unsqueeze165_out1[..],
            &constant1029_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape56_out1 = slice70_out1.reshape(concat71_out1);
        let unsqueeze166_out1 = [sub10_out1 as i64];
        let unsqueeze167_out1 = [gather57_out1 as i64];
        let slice71_out1 = constant474_out1
            .slice(s![unsqueeze166_out1[0]..unsqueeze167_out1[0], ..]);
        let reshape57_out1 = slice71_out1.reshape(concat72_out1);
        let cast84_out1 = reshape56_out1;
        let cast85_out1 = reshape57_out1;
        let mul66_out1 = squeeze22_out1.clone().mul(cast84_out1);
        let shape73_out1: [i64; 4] = {
            let axes = &squeeze22_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather58_out1 = shape73_out1[3] as i64;
        let constant1035_out1 = 2i64;
        let div31_out1 = gather58_out1 / constant1035_out1;
        let cast86_out1 = div31_out1;
        let cast87_out1 = cast86_out1;
        let unsqueeze168_out1 = [cast87_out1 as i64];
        let slice72_out1 = squeeze22_out1
            .clone()
            .slice(s![.., .., .., unsqueeze168_out1[0]..9223372036854775807]);
        let neg15_out1 = slice72_out1.neg();
        let unsqueeze169_out1 = [cast87_out1 as i64];
        let slice73_out1 = squeeze22_out1.slice(s![.., .., .., 0..unsqueeze169_out1[0]]);
        let concat73_out1 = burn::tensor::Tensor::cat(
            [neg15_out1, slice73_out1].into(),
            3,
        );
        let mul67_out1 = concat73_out1.mul(cast85_out1);
        let add56_out1 = mul66_out1.add(mul67_out1);
        let cast88_out1 = reshape54_out1;
        let cast89_out1 = reshape55_out1;
        let mul68_out1 = squeeze23_out1.clone().mul(cast88_out1);
        let shape74_out1: [i64; 4] = {
            let axes = &squeeze23_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather59_out1 = shape74_out1[3] as i64;
        let constant1045_out1 = 2i64;
        let div32_out1 = gather59_out1 / constant1045_out1;
        let cast90_out1 = div32_out1;
        let cast91_out1 = cast90_out1;
        let unsqueeze170_out1 = [cast91_out1 as i64];
        let slice74_out1 = squeeze23_out1
            .clone()
            .slice(s![.., .., .., unsqueeze170_out1[0]..9223372036854775807]);
        let neg16_out1 = slice74_out1.neg();
        let unsqueeze171_out1 = [cast91_out1 as i64];
        let slice75_out1 = squeeze23_out1.slice(s![.., .., .., 0..unsqueeze171_out1[0]]);
        let concat74_out1 = burn::tensor::Tensor::cat(
            [neg16_out1, slice75_out1].into(),
            3,
        );
        let mul69_out1 = concat74_out1.mul(cast89_out1);
        let add57_out1 = mul68_out1.add(mul69_out1);
        let shape75_out1: [i64; 4] = {
            let axes = &add56_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let slice76_out1: [i64; 1] = shape75_out1[3..4].try_into().unwrap();
        let cast92_out1 = {
            let shape_array = slice76_out1 as [i64; 1usize];
            let float_array: [f64; 1usize] = shape_array.map(|x| x as f64);
            Tensor::<
                1,
            >::from_data(
                TensorData::from(float_array),
                (&self.device, burn::tensor::DType::F32),
            )
        };
        let sqrt22_out1 = cast92_out1.sqrt();
        let constant1056_out1 = self.constant1056.val();
        let div33_out1 = constant1056_out1.div(sqrt22_out1);
        let cast93_out1 = div33_out1;
        let transpose23_out1 = add57_out1.permute([0, 1, 3, 2]);
        let sqrt23_out1 = cast93_out1.clone().sqrt();
        let mul70_out1 = add56_out1
            .mul((sqrt23_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let sqrt24_out1 = cast93_out1.sqrt();
        let mul71_out1 = transpose23_out1
            .mul((sqrt24_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let matmul45_out1 = mul70_out1.matmul(mul71_out1);
        let constant1058_out1 = self.constant1058.val();
        let constant1057_out1 = self.constant1057.val();
        let where21_out1 = constant1058_out1
            .unsqueeze_dims(&[0isize, 1isize, 2isize])
            .mask_where(
                less4_out1,
                constant1057_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let add58_out1 = matmul45_out1.add(where21_out1);
        let softmax8_out1 = burn::tensor::activation::softmax(add58_out1, 3);
        let isnan8_out1 = softmax8_out1.clone().is_nan();
        let constant1059_out1 = self.constant1059.val();
        let where22_out1 = softmax8_out1
            .mask_where(
                isnan8_out1,
                constant1059_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let matmul46_out1 = where22_out1.matmul(squeeze24_out1);
        let transpose24_out1 = matmul46_out1.permute([0, 2, 1, 3]);
        let unsqueeze172_out1 = [gather54_out1 as i64];
        let unsqueeze173_out1 = [gather55_out1 as i64];
        let constant1062_out1: [i64; 1] = [512i64];
        let concat75_out1: [i64; 3usize] = [
            &unsqueeze172_out1[..],
            &unsqueeze173_out1[..],
            &constant1062_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape58_out1 = transpose24_out1.reshape(concat75_out1);
        let linear31_out1 = self.linear31.forward(reshape58_out1);
        let add59_out1 = add55_out1.add(linear31_out1);
        add59_out1
    }
}
#[derive(Module, Debug)]
pub struct Submodule10 {
    layernormalization17: LayerNorm,
    linear32: Linear,
    constant1063: burn::module::Param<Tensor<1>>,
    constant1064: burn::module::Param<Tensor<1>>,
    constant1065: burn::module::Param<Tensor<1>>,
    linear33: Linear,
    layernormalization18: LayerNorm,
    linear34: Linear,
    constant1133: burn::module::Param<Tensor<1>>,
    constant1135: burn::module::Param<Tensor<1>>,
    constant1134: burn::module::Param<Tensor<1>>,
    constant1136: burn::module::Param<Tensor<1>>,
    linear35: Linear,
    #[module(skip)]
    device: Device,
}
impl Submodule10 {
    #[allow(unused_variables)]
    pub fn new(device: &Device) -> Self {
        let layernormalization17 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear32 = LinearConfig::new(512, 2048).with_bias(true).init(device);
        let constant1063: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1.4142135381698608f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1064: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1065: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([0.5f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let linear33 = LinearConfig::new(2048, 512).with_bias(true).init(device);
        let layernormalization18 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear34 = LinearConfig::new(512, 1536).with_bias(false).init(device);
        let constant1133: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1135: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1134: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1136: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let linear35 = LinearConfig::new(512, 512).with_bias(true).init(device);
        Self {
            layernormalization17,
            linear32,
            constant1063,
            constant1064,
            constant1065,
            linear33,
            layernormalization18,
            linear34,
            constant1133,
            constant1135,
            constant1134,
            constant1136,
            linear35,
            device: device.clone(),
        }
    }
    #[allow(clippy::let_and_return, clippy::approx_constant)]
    pub fn forward(
        &self,
        add59_out1: Tensor<3>,
        constant461_out1: Tensor<2>,
        constant474_out1: Tensor<2>,
        less4_out1: Tensor<4, Bool>,
    ) -> Tensor<3> {
        let layernormalization17_out1 = {
            let dtype = add59_out1.clone().dtype();
            self.layernormalization17
                .forward(add59_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let linear32_out1 = self.linear32.forward(layernormalization17_out1);
        let constant1063_out1 = self.constant1063.val();
        let div34_out1 = linear32_out1
            .clone()
            .div((constant1063_out1).unsqueeze_dims(&[0isize, 1isize]));
        let erf8_out1 = div34_out1.erf();
        let constant1064_out1 = self.constant1064.val();
        let add60_out1 = erf8_out1
            .add((constant1064_out1).unsqueeze_dims(&[0isize, 1isize]));
        let mul72_out1 = linear32_out1.mul(add60_out1);
        let constant1065_out1 = self.constant1065.val();
        let mul73_out1 = mul72_out1
            .mul((constant1065_out1).unsqueeze_dims(&[0isize, 1isize]));
        let linear33_out1 = self.linear33.forward(mul73_out1);
        let add61_out1 = add59_out1.add(linear33_out1);
        let layernormalization18_out1 = {
            let dtype = add61_out1.clone().dtype();
            self.layernormalization18
                .forward(add61_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let shape76_out1: [i64; 3] = {
            let axes = &layernormalization18_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather60_out1 = shape76_out1[0] as i64;
        let shape77_out1: [i64; 3] = {
            let axes = &layernormalization18_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather61_out1 = shape77_out1[1] as i64;
        let linear34_out1 = self.linear34.forward(layernormalization18_out1);
        let unsqueeze174_out1 = [gather60_out1 as i64];
        let unsqueeze175_out1 = [gather61_out1 as i64];
        let constant1072_out1: [i64; 1] = [64i64];
        let constant1070_out1: [i64; 1] = [3i64];
        let constant1071_out1: [i64; 1] = [8i64];
        let concat76_out1: [i64; 5usize] = [
            &unsqueeze174_out1[..],
            &unsqueeze175_out1[..],
            &constant1070_out1[..],
            &constant1071_out1[..],
            &constant1072_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape59_out1 = linear34_out1.reshape(concat76_out1);
        let transpose25_out1 = reshape59_out1.permute([2, 0, 3, 1, 4]);
        let split_tensors = transpose25_out1.split_with_sizes([1, 1, 1].into(), 0);
        let [split9_out1, split9_out2, split9_out3] = split_tensors.try_into().unwrap();
        let squeeze25_out1 = split9_out1.squeeze_dims::<4>(&[0]);
        let squeeze26_out1 = split9_out2.squeeze_dims::<4>(&[0]);
        let squeeze27_out1 = split9_out3.squeeze_dims::<4>(&[0]);
        let shape78_out1: [i64; 4] = {
            let axes = &squeeze25_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather62_out1 = shape78_out1[2] as i64;
        let shape79_out1: [i64; 4] = {
            let axes = &squeeze26_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather63_out1 = shape79_out1[2] as i64;
        let sub11_out1 = gather63_out1 - gather62_out1;
        let unsqueeze176_out1 = [gather63_out1 as i64];
        let slice77_out1 = constant461_out1
            .clone()
            .slice(s![0..unsqueeze176_out1[0], ..]);
        let unsqueeze177_out1 = [gather63_out1 as i64];
        let constant1086_out1: [i64; 1] = [64i64];
        let constant1083_out1: [i64; 1] = [1i64];
        let constant1084_out1: [i64; 1] = [1i64];
        let concat77_out1: [i64; 4usize] = [
            &constant1083_out1[..],
            &constant1084_out1[..],
            &unsqueeze177_out1[..],
            &constant1086_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze178_out1 = [gather63_out1 as i64];
        let constant1090_out1: [i64; 1] = [64i64];
        let constant1087_out1: [i64; 1] = [1i64];
        let constant1088_out1: [i64; 1] = [1i64];
        let concat78_out1: [i64; 4usize] = [
            &constant1087_out1[..],
            &constant1088_out1[..],
            &unsqueeze178_out1[..],
            &constant1090_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape60_out1 = slice77_out1.reshape(concat77_out1);
        let unsqueeze179_out1 = [gather63_out1 as i64];
        let slice78_out1 = constant474_out1
            .clone()
            .slice(s![0..unsqueeze179_out1[0], ..]);
        let reshape61_out1 = slice78_out1.reshape(concat78_out1);
        let unsqueeze180_out1 = [sub11_out1 as i64];
        let unsqueeze181_out1 = [gather63_out1 as i64];
        let slice79_out1 = constant461_out1
            .slice(s![unsqueeze180_out1[0]..unsqueeze181_out1[0], ..]);
        let unsqueeze182_out1 = [gather62_out1 as i64];
        let constant1102_out1: [i64; 1] = [64i64];
        let constant1099_out1: [i64; 1] = [1i64];
        let constant1100_out1: [i64; 1] = [1i64];
        let concat79_out1: [i64; 4usize] = [
            &constant1099_out1[..],
            &constant1100_out1[..],
            &unsqueeze182_out1[..],
            &constant1102_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze183_out1 = [gather62_out1 as i64];
        let constant1106_out1: [i64; 1] = [64i64];
        let constant1103_out1: [i64; 1] = [1i64];
        let constant1104_out1: [i64; 1] = [1i64];
        let concat80_out1: [i64; 4usize] = [
            &constant1103_out1[..],
            &constant1104_out1[..],
            &unsqueeze183_out1[..],
            &constant1106_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape62_out1 = slice79_out1.reshape(concat79_out1);
        let unsqueeze184_out1 = [sub11_out1 as i64];
        let unsqueeze185_out1 = [gather63_out1 as i64];
        let slice80_out1 = constant474_out1
            .slice(s![unsqueeze184_out1[0]..unsqueeze185_out1[0], ..]);
        let reshape63_out1 = slice80_out1.reshape(concat80_out1);
        let cast94_out1 = reshape62_out1;
        let cast95_out1 = reshape63_out1;
        let mul74_out1 = squeeze25_out1.clone().mul(cast94_out1);
        let shape80_out1: [i64; 4] = {
            let axes = &squeeze25_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather64_out1 = shape80_out1[3] as i64;
        let constant1112_out1 = 2i64;
        let div35_out1 = gather64_out1 / constant1112_out1;
        let cast96_out1 = div35_out1;
        let cast97_out1 = cast96_out1;
        let unsqueeze186_out1 = [cast97_out1 as i64];
        let slice81_out1 = squeeze25_out1
            .clone()
            .slice(s![.., .., .., unsqueeze186_out1[0]..9223372036854775807]);
        let neg17_out1 = slice81_out1.neg();
        let unsqueeze187_out1 = [cast97_out1 as i64];
        let slice82_out1 = squeeze25_out1.slice(s![.., .., .., 0..unsqueeze187_out1[0]]);
        let concat81_out1 = burn::tensor::Tensor::cat(
            [neg17_out1, slice82_out1].into(),
            3,
        );
        let mul75_out1 = concat81_out1.mul(cast95_out1);
        let add62_out1 = mul74_out1.add(mul75_out1);
        let cast98_out1 = reshape60_out1;
        let cast99_out1 = reshape61_out1;
        let mul76_out1 = squeeze26_out1.clone().mul(cast98_out1);
        let shape81_out1: [i64; 4] = {
            let axes = &squeeze26_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather65_out1 = shape81_out1[3] as i64;
        let constant1122_out1 = 2i64;
        let div36_out1 = gather65_out1 / constant1122_out1;
        let cast100_out1 = div36_out1;
        let cast101_out1 = cast100_out1;
        let unsqueeze188_out1 = [cast101_out1 as i64];
        let slice83_out1 = squeeze26_out1
            .clone()
            .slice(s![.., .., .., unsqueeze188_out1[0]..9223372036854775807]);
        let neg18_out1 = slice83_out1.neg();
        let unsqueeze189_out1 = [cast101_out1 as i64];
        let slice84_out1 = squeeze26_out1.slice(s![.., .., .., 0..unsqueeze189_out1[0]]);
        let concat82_out1 = burn::tensor::Tensor::cat(
            [neg18_out1, slice84_out1].into(),
            3,
        );
        let mul77_out1 = concat82_out1.mul(cast99_out1);
        let add63_out1 = mul76_out1.add(mul77_out1);
        let shape82_out1: [i64; 4] = {
            let axes = &add62_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let slice85_out1: [i64; 1] = shape82_out1[3..4].try_into().unwrap();
        let cast102_out1 = {
            let shape_array = slice85_out1 as [i64; 1usize];
            let float_array: [f64; 1usize] = shape_array.map(|x| x as f64);
            Tensor::<
                1,
            >::from_data(
                TensorData::from(float_array),
                (&self.device, burn::tensor::DType::F32),
            )
        };
        let sqrt25_out1 = cast102_out1.sqrt();
        let constant1133_out1 = self.constant1133.val();
        let div37_out1 = constant1133_out1.div(sqrt25_out1);
        let cast103_out1 = div37_out1;
        let transpose26_out1 = add63_out1.permute([0, 1, 3, 2]);
        let sqrt26_out1 = cast103_out1.clone().sqrt();
        let mul78_out1 = add62_out1
            .mul((sqrt26_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let sqrt27_out1 = cast103_out1.sqrt();
        let mul79_out1 = transpose26_out1
            .mul((sqrt27_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let matmul51_out1 = mul78_out1.matmul(mul79_out1);
        let constant1135_out1 = self.constant1135.val();
        let constant1134_out1 = self.constant1134.val();
        let where23_out1 = constant1135_out1
            .unsqueeze_dims(&[0isize, 1isize, 2isize])
            .mask_where(
                less4_out1,
                constant1134_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let add64_out1 = matmul51_out1.add(where23_out1);
        let softmax9_out1 = burn::tensor::activation::softmax(add64_out1, 3);
        let isnan9_out1 = softmax9_out1.clone().is_nan();
        let constant1136_out1 = self.constant1136.val();
        let where24_out1 = softmax9_out1
            .mask_where(
                isnan9_out1,
                constant1136_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let matmul52_out1 = where24_out1.matmul(squeeze27_out1);
        let transpose27_out1 = matmul52_out1.permute([0, 2, 1, 3]);
        let unsqueeze190_out1 = [gather60_out1 as i64];
        let unsqueeze191_out1 = [gather61_out1 as i64];
        let constant1139_out1: [i64; 1] = [512i64];
        let concat83_out1: [i64; 3usize] = [
            &unsqueeze190_out1[..],
            &unsqueeze191_out1[..],
            &constant1139_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape64_out1 = transpose27_out1.reshape(concat83_out1);
        let linear35_out1 = self.linear35.forward(reshape64_out1);
        let add65_out1 = add61_out1.add(linear35_out1);
        add65_out1
    }
}
#[derive(Module, Debug)]
pub struct Submodule11 {
    layernormalization19: LayerNorm,
    linear36: Linear,
    constant1140: burn::module::Param<Tensor<1>>,
    constant1141: burn::module::Param<Tensor<1>>,
    constant1142: burn::module::Param<Tensor<1>>,
    linear37: Linear,
    layernormalization20: LayerNorm,
    linear38: Linear,
    constant1210: burn::module::Param<Tensor<1>>,
    constant1212: burn::module::Param<Tensor<1>>,
    constant1211: burn::module::Param<Tensor<1>>,
    constant1213: burn::module::Param<Tensor<1>>,
    linear39: Linear,
    #[module(skip)]
    device: Device,
}
impl Submodule11 {
    #[allow(unused_variables)]
    pub fn new(device: &Device) -> Self {
        let layernormalization19 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear36 = LinearConfig::new(512, 2048).with_bias(true).init(device);
        let constant1140: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1.4142135381698608f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1141: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1142: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([0.5f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let linear37 = LinearConfig::new(2048, 512).with_bias(true).init(device);
        let layernormalization20 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear38 = LinearConfig::new(512, 1536).with_bias(false).init(device);
        let constant1210: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1212: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1211: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1213: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let linear39 = LinearConfig::new(512, 512).with_bias(true).init(device);
        Self {
            layernormalization19,
            linear36,
            constant1140,
            constant1141,
            constant1142,
            linear37,
            layernormalization20,
            linear38,
            constant1210,
            constant1212,
            constant1211,
            constant1213,
            linear39,
            device: device.clone(),
        }
    }
    #[allow(clippy::let_and_return, clippy::approx_constant)]
    pub fn forward(
        &self,
        add65_out1: Tensor<3>,
        constant461_out1: Tensor<2>,
        constant474_out1: Tensor<2>,
        less4_out1: Tensor<4, Bool>,
    ) -> Tensor<3> {
        let layernormalization19_out1 = {
            let dtype = add65_out1.clone().dtype();
            self.layernormalization19
                .forward(add65_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let linear36_out1 = self.linear36.forward(layernormalization19_out1);
        let constant1140_out1 = self.constant1140.val();
        let div38_out1 = linear36_out1
            .clone()
            .div((constant1140_out1).unsqueeze_dims(&[0isize, 1isize]));
        let erf9_out1 = div38_out1.erf();
        let constant1141_out1 = self.constant1141.val();
        let add66_out1 = erf9_out1
            .add((constant1141_out1).unsqueeze_dims(&[0isize, 1isize]));
        let mul80_out1 = linear36_out1.mul(add66_out1);
        let constant1142_out1 = self.constant1142.val();
        let mul81_out1 = mul80_out1
            .mul((constant1142_out1).unsqueeze_dims(&[0isize, 1isize]));
        let linear37_out1 = self.linear37.forward(mul81_out1);
        let add67_out1 = add65_out1.add(linear37_out1);
        let layernormalization20_out1 = {
            let dtype = add67_out1.clone().dtype();
            self.layernormalization20
                .forward(add67_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let shape83_out1: [i64; 3] = {
            let axes = &layernormalization20_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather66_out1 = shape83_out1[0] as i64;
        let shape84_out1: [i64; 3] = {
            let axes = &layernormalization20_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather67_out1 = shape84_out1[1] as i64;
        let linear38_out1 = self.linear38.forward(layernormalization20_out1);
        let unsqueeze192_out1 = [gather66_out1 as i64];
        let unsqueeze193_out1 = [gather67_out1 as i64];
        let constant1149_out1: [i64; 1] = [64i64];
        let constant1147_out1: [i64; 1] = [3i64];
        let constant1148_out1: [i64; 1] = [8i64];
        let concat84_out1: [i64; 5usize] = [
            &unsqueeze192_out1[..],
            &unsqueeze193_out1[..],
            &constant1147_out1[..],
            &constant1148_out1[..],
            &constant1149_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape65_out1 = linear38_out1.reshape(concat84_out1);
        let transpose28_out1 = reshape65_out1.permute([2, 0, 3, 1, 4]);
        let split_tensors = transpose28_out1.split_with_sizes([1, 1, 1].into(), 0);
        let [split10_out1, split10_out2, split10_out3] = split_tensors
            .try_into()
            .unwrap();
        let squeeze28_out1 = split10_out1.squeeze_dims::<4>(&[0]);
        let squeeze29_out1 = split10_out2.squeeze_dims::<4>(&[0]);
        let squeeze30_out1 = split10_out3.squeeze_dims::<4>(&[0]);
        let shape85_out1: [i64; 4] = {
            let axes = &squeeze28_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather68_out1 = shape85_out1[2] as i64;
        let shape86_out1: [i64; 4] = {
            let axes = &squeeze29_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather69_out1 = shape86_out1[2] as i64;
        let sub12_out1 = gather69_out1 - gather68_out1;
        let unsqueeze194_out1 = [gather69_out1 as i64];
        let slice86_out1 = constant461_out1
            .clone()
            .slice(s![0..unsqueeze194_out1[0], ..]);
        let unsqueeze195_out1 = [gather69_out1 as i64];
        let constant1163_out1: [i64; 1] = [64i64];
        let constant1160_out1: [i64; 1] = [1i64];
        let constant1161_out1: [i64; 1] = [1i64];
        let concat85_out1: [i64; 4usize] = [
            &constant1160_out1[..],
            &constant1161_out1[..],
            &unsqueeze195_out1[..],
            &constant1163_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze196_out1 = [gather69_out1 as i64];
        let constant1167_out1: [i64; 1] = [64i64];
        let constant1164_out1: [i64; 1] = [1i64];
        let constant1165_out1: [i64; 1] = [1i64];
        let concat86_out1: [i64; 4usize] = [
            &constant1164_out1[..],
            &constant1165_out1[..],
            &unsqueeze196_out1[..],
            &constant1167_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape66_out1 = slice86_out1.reshape(concat85_out1);
        let unsqueeze197_out1 = [gather69_out1 as i64];
        let slice87_out1 = constant474_out1
            .clone()
            .slice(s![0..unsqueeze197_out1[0], ..]);
        let reshape67_out1 = slice87_out1.reshape(concat86_out1);
        let unsqueeze198_out1 = [sub12_out1 as i64];
        let unsqueeze199_out1 = [gather69_out1 as i64];
        let slice88_out1 = constant461_out1
            .slice(s![unsqueeze198_out1[0]..unsqueeze199_out1[0], ..]);
        let unsqueeze200_out1 = [gather68_out1 as i64];
        let constant1179_out1: [i64; 1] = [64i64];
        let constant1176_out1: [i64; 1] = [1i64];
        let constant1177_out1: [i64; 1] = [1i64];
        let concat87_out1: [i64; 4usize] = [
            &constant1176_out1[..],
            &constant1177_out1[..],
            &unsqueeze200_out1[..],
            &constant1179_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze201_out1 = [gather68_out1 as i64];
        let constant1183_out1: [i64; 1] = [64i64];
        let constant1180_out1: [i64; 1] = [1i64];
        let constant1181_out1: [i64; 1] = [1i64];
        let concat88_out1: [i64; 4usize] = [
            &constant1180_out1[..],
            &constant1181_out1[..],
            &unsqueeze201_out1[..],
            &constant1183_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape68_out1 = slice88_out1.reshape(concat87_out1);
        let unsqueeze202_out1 = [sub12_out1 as i64];
        let unsqueeze203_out1 = [gather69_out1 as i64];
        let slice89_out1 = constant474_out1
            .slice(s![unsqueeze202_out1[0]..unsqueeze203_out1[0], ..]);
        let reshape69_out1 = slice89_out1.reshape(concat88_out1);
        let cast104_out1 = reshape68_out1;
        let cast105_out1 = reshape69_out1;
        let mul82_out1 = squeeze28_out1.clone().mul(cast104_out1);
        let shape87_out1: [i64; 4] = {
            let axes = &squeeze28_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather70_out1 = shape87_out1[3] as i64;
        let constant1189_out1 = 2i64;
        let div39_out1 = gather70_out1 / constant1189_out1;
        let cast106_out1 = div39_out1;
        let cast107_out1 = cast106_out1;
        let unsqueeze204_out1 = [cast107_out1 as i64];
        let slice90_out1 = squeeze28_out1
            .clone()
            .slice(s![.., .., .., unsqueeze204_out1[0]..9223372036854775807]);
        let neg19_out1 = slice90_out1.neg();
        let unsqueeze205_out1 = [cast107_out1 as i64];
        let slice91_out1 = squeeze28_out1.slice(s![.., .., .., 0..unsqueeze205_out1[0]]);
        let concat89_out1 = burn::tensor::Tensor::cat(
            [neg19_out1, slice91_out1].into(),
            3,
        );
        let mul83_out1 = concat89_out1.mul(cast105_out1);
        let add68_out1 = mul82_out1.add(mul83_out1);
        let cast108_out1 = reshape66_out1;
        let cast109_out1 = reshape67_out1;
        let mul84_out1 = squeeze29_out1.clone().mul(cast108_out1);
        let shape88_out1: [i64; 4] = {
            let axes = &squeeze29_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather71_out1 = shape88_out1[3] as i64;
        let constant1199_out1 = 2i64;
        let div40_out1 = gather71_out1 / constant1199_out1;
        let cast110_out1 = div40_out1;
        let cast111_out1 = cast110_out1;
        let unsqueeze206_out1 = [cast111_out1 as i64];
        let slice92_out1 = squeeze29_out1
            .clone()
            .slice(s![.., .., .., unsqueeze206_out1[0]..9223372036854775807]);
        let neg20_out1 = slice92_out1.neg();
        let unsqueeze207_out1 = [cast111_out1 as i64];
        let slice93_out1 = squeeze29_out1.slice(s![.., .., .., 0..unsqueeze207_out1[0]]);
        let concat90_out1 = burn::tensor::Tensor::cat(
            [neg20_out1, slice93_out1].into(),
            3,
        );
        let mul85_out1 = concat90_out1.mul(cast109_out1);
        let add69_out1 = mul84_out1.add(mul85_out1);
        let shape89_out1: [i64; 4] = {
            let axes = &add68_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let slice94_out1: [i64; 1] = shape89_out1[3..4].try_into().unwrap();
        let cast112_out1 = {
            let shape_array = slice94_out1 as [i64; 1usize];
            let float_array: [f64; 1usize] = shape_array.map(|x| x as f64);
            Tensor::<
                1,
            >::from_data(
                TensorData::from(float_array),
                (&self.device, burn::tensor::DType::F32),
            )
        };
        let sqrt28_out1 = cast112_out1.sqrt();
        let constant1210_out1 = self.constant1210.val();
        let div41_out1 = constant1210_out1.div(sqrt28_out1);
        let cast113_out1 = div41_out1;
        let transpose29_out1 = add69_out1.permute([0, 1, 3, 2]);
        let sqrt29_out1 = cast113_out1.clone().sqrt();
        let mul86_out1 = add68_out1
            .mul((sqrt29_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let sqrt30_out1 = cast113_out1.sqrt();
        let mul87_out1 = transpose29_out1
            .mul((sqrt30_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let matmul57_out1 = mul86_out1.matmul(mul87_out1);
        let constant1212_out1 = self.constant1212.val();
        let constant1211_out1 = self.constant1211.val();
        let where25_out1 = constant1212_out1
            .unsqueeze_dims(&[0isize, 1isize, 2isize])
            .mask_where(
                less4_out1,
                constant1211_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let add70_out1 = matmul57_out1.add(where25_out1);
        let softmax10_out1 = burn::tensor::activation::softmax(add70_out1, 3);
        let isnan10_out1 = softmax10_out1.clone().is_nan();
        let constant1213_out1 = self.constant1213.val();
        let where26_out1 = softmax10_out1
            .mask_where(
                isnan10_out1,
                constant1213_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let matmul58_out1 = where26_out1.matmul(squeeze30_out1);
        let transpose30_out1 = matmul58_out1.permute([0, 2, 1, 3]);
        let unsqueeze208_out1 = [gather66_out1 as i64];
        let unsqueeze209_out1 = [gather67_out1 as i64];
        let constant1216_out1: [i64; 1] = [512i64];
        let concat91_out1: [i64; 3usize] = [
            &unsqueeze208_out1[..],
            &unsqueeze209_out1[..],
            &constant1216_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape70_out1 = transpose30_out1.reshape(concat91_out1);
        let linear39_out1 = self.linear39.forward(reshape70_out1);
        let add71_out1 = add67_out1.add(linear39_out1);
        add71_out1
    }
}
#[derive(Module, Debug)]
pub struct Submodule12 {
    layernormalization21: LayerNorm,
    linear40: Linear,
    constant1217: burn::module::Param<Tensor<1>>,
    constant1218: burn::module::Param<Tensor<1>>,
    constant1219: burn::module::Param<Tensor<1>>,
    linear41: Linear,
    layernormalization22: LayerNorm,
    linear42: Linear,
    constant1287: burn::module::Param<Tensor<1>>,
    constant1289: burn::module::Param<Tensor<1>>,
    constant1288: burn::module::Param<Tensor<1>>,
    constant1290: burn::module::Param<Tensor<1>>,
    linear43: Linear,
    #[module(skip)]
    device: Device,
}
impl Submodule12 {
    #[allow(unused_variables)]
    pub fn new(device: &Device) -> Self {
        let layernormalization21 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear40 = LinearConfig::new(512, 2048).with_bias(true).init(device);
        let constant1217: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1.4142135381698608f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1218: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1219: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([0.5f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let linear41 = LinearConfig::new(2048, 512).with_bias(true).init(device);
        let layernormalization22 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear42 = LinearConfig::new(512, 1536).with_bias(false).init(device);
        let constant1287: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1289: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1288: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1290: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let linear43 = LinearConfig::new(512, 512).with_bias(true).init(device);
        Self {
            layernormalization21,
            linear40,
            constant1217,
            constant1218,
            constant1219,
            linear41,
            layernormalization22,
            linear42,
            constant1287,
            constant1289,
            constant1288,
            constant1290,
            linear43,
            device: device.clone(),
        }
    }
    #[allow(clippy::let_and_return, clippy::approx_constant)]
    pub fn forward(
        &self,
        add71_out1: Tensor<3>,
        constant461_out1: Tensor<2>,
        constant474_out1: Tensor<2>,
        less4_out1: Tensor<4, Bool>,
    ) -> Tensor<3> {
        let layernormalization21_out1 = {
            let dtype = add71_out1.clone().dtype();
            self.layernormalization21
                .forward(add71_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let linear40_out1 = self.linear40.forward(layernormalization21_out1);
        let constant1217_out1 = self.constant1217.val();
        let div42_out1 = linear40_out1
            .clone()
            .div((constant1217_out1).unsqueeze_dims(&[0isize, 1isize]));
        let erf10_out1 = div42_out1.erf();
        let constant1218_out1 = self.constant1218.val();
        let add72_out1 = erf10_out1
            .add((constant1218_out1).unsqueeze_dims(&[0isize, 1isize]));
        let mul88_out1 = linear40_out1.mul(add72_out1);
        let constant1219_out1 = self.constant1219.val();
        let mul89_out1 = mul88_out1
            .mul((constant1219_out1).unsqueeze_dims(&[0isize, 1isize]));
        let linear41_out1 = self.linear41.forward(mul89_out1);
        let add73_out1 = add71_out1.add(linear41_out1);
        let layernormalization22_out1 = {
            let dtype = add73_out1.clone().dtype();
            self.layernormalization22
                .forward(add73_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let shape90_out1: [i64; 3] = {
            let axes = &layernormalization22_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather72_out1 = shape90_out1[0] as i64;
        let shape91_out1: [i64; 3] = {
            let axes = &layernormalization22_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather73_out1 = shape91_out1[1] as i64;
        let linear42_out1 = self.linear42.forward(layernormalization22_out1);
        let unsqueeze210_out1 = [gather72_out1 as i64];
        let unsqueeze211_out1 = [gather73_out1 as i64];
        let constant1226_out1: [i64; 1] = [64i64];
        let constant1224_out1: [i64; 1] = [3i64];
        let constant1225_out1: [i64; 1] = [8i64];
        let concat92_out1: [i64; 5usize] = [
            &unsqueeze210_out1[..],
            &unsqueeze211_out1[..],
            &constant1224_out1[..],
            &constant1225_out1[..],
            &constant1226_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape71_out1 = linear42_out1.reshape(concat92_out1);
        let transpose31_out1 = reshape71_out1.permute([2, 0, 3, 1, 4]);
        let split_tensors = transpose31_out1.split_with_sizes([1, 1, 1].into(), 0);
        let [split11_out1, split11_out2, split11_out3] = split_tensors
            .try_into()
            .unwrap();
        let squeeze31_out1 = split11_out1.squeeze_dims::<4>(&[0]);
        let squeeze32_out1 = split11_out2.squeeze_dims::<4>(&[0]);
        let squeeze33_out1 = split11_out3.squeeze_dims::<4>(&[0]);
        let shape92_out1: [i64; 4] = {
            let axes = &squeeze31_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather74_out1 = shape92_out1[2] as i64;
        let shape93_out1: [i64; 4] = {
            let axes = &squeeze32_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather75_out1 = shape93_out1[2] as i64;
        let sub13_out1 = gather75_out1 - gather74_out1;
        let unsqueeze212_out1 = [gather75_out1 as i64];
        let slice95_out1 = constant461_out1
            .clone()
            .slice(s![0..unsqueeze212_out1[0], ..]);
        let unsqueeze213_out1 = [gather75_out1 as i64];
        let constant1240_out1: [i64; 1] = [64i64];
        let constant1237_out1: [i64; 1] = [1i64];
        let constant1238_out1: [i64; 1] = [1i64];
        let concat93_out1: [i64; 4usize] = [
            &constant1237_out1[..],
            &constant1238_out1[..],
            &unsqueeze213_out1[..],
            &constant1240_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze214_out1 = [gather75_out1 as i64];
        let constant1244_out1: [i64; 1] = [64i64];
        let constant1241_out1: [i64; 1] = [1i64];
        let constant1242_out1: [i64; 1] = [1i64];
        let concat94_out1: [i64; 4usize] = [
            &constant1241_out1[..],
            &constant1242_out1[..],
            &unsqueeze214_out1[..],
            &constant1244_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape72_out1 = slice95_out1.reshape(concat93_out1);
        let unsqueeze215_out1 = [gather75_out1 as i64];
        let slice96_out1 = constant474_out1
            .clone()
            .slice(s![0..unsqueeze215_out1[0], ..]);
        let reshape73_out1 = slice96_out1.reshape(concat94_out1);
        let unsqueeze216_out1 = [sub13_out1 as i64];
        let unsqueeze217_out1 = [gather75_out1 as i64];
        let slice97_out1 = constant461_out1
            .slice(s![unsqueeze216_out1[0]..unsqueeze217_out1[0], ..]);
        let unsqueeze218_out1 = [gather74_out1 as i64];
        let constant1256_out1: [i64; 1] = [64i64];
        let constant1253_out1: [i64; 1] = [1i64];
        let constant1254_out1: [i64; 1] = [1i64];
        let concat95_out1: [i64; 4usize] = [
            &constant1253_out1[..],
            &constant1254_out1[..],
            &unsqueeze218_out1[..],
            &constant1256_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze219_out1 = [gather74_out1 as i64];
        let constant1260_out1: [i64; 1] = [64i64];
        let constant1257_out1: [i64; 1] = [1i64];
        let constant1258_out1: [i64; 1] = [1i64];
        let concat96_out1: [i64; 4usize] = [
            &constant1257_out1[..],
            &constant1258_out1[..],
            &unsqueeze219_out1[..],
            &constant1260_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape74_out1 = slice97_out1.reshape(concat95_out1);
        let unsqueeze220_out1 = [sub13_out1 as i64];
        let unsqueeze221_out1 = [gather75_out1 as i64];
        let slice98_out1 = constant474_out1
            .slice(s![unsqueeze220_out1[0]..unsqueeze221_out1[0], ..]);
        let reshape75_out1 = slice98_out1.reshape(concat96_out1);
        let cast114_out1 = reshape74_out1;
        let cast115_out1 = reshape75_out1;
        let mul90_out1 = squeeze31_out1.clone().mul(cast114_out1);
        let shape94_out1: [i64; 4] = {
            let axes = &squeeze31_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather76_out1 = shape94_out1[3] as i64;
        let constant1266_out1 = 2i64;
        let div43_out1 = gather76_out1 / constant1266_out1;
        let cast116_out1 = div43_out1;
        let cast117_out1 = cast116_out1;
        let unsqueeze222_out1 = [cast117_out1 as i64];
        let slice99_out1 = squeeze31_out1
            .clone()
            .slice(s![.., .., .., unsqueeze222_out1[0]..9223372036854775807]);
        let neg21_out1 = slice99_out1.neg();
        let unsqueeze223_out1 = [cast117_out1 as i64];
        let slice100_out1 = squeeze31_out1
            .slice(s![.., .., .., 0..unsqueeze223_out1[0]]);
        let concat97_out1 = burn::tensor::Tensor::cat(
            [neg21_out1, slice100_out1].into(),
            3,
        );
        let mul91_out1 = concat97_out1.mul(cast115_out1);
        let add74_out1 = mul90_out1.add(mul91_out1);
        let cast118_out1 = reshape72_out1;
        let cast119_out1 = reshape73_out1;
        let mul92_out1 = squeeze32_out1.clone().mul(cast118_out1);
        let shape95_out1: [i64; 4] = {
            let axes = &squeeze32_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather77_out1 = shape95_out1[3] as i64;
        let constant1276_out1 = 2i64;
        let div44_out1 = gather77_out1 / constant1276_out1;
        let cast120_out1 = div44_out1;
        let cast121_out1 = cast120_out1;
        let unsqueeze224_out1 = [cast121_out1 as i64];
        let slice101_out1 = squeeze32_out1
            .clone()
            .slice(s![.., .., .., unsqueeze224_out1[0]..9223372036854775807]);
        let neg22_out1 = slice101_out1.neg();
        let unsqueeze225_out1 = [cast121_out1 as i64];
        let slice102_out1 = squeeze32_out1
            .slice(s![.., .., .., 0..unsqueeze225_out1[0]]);
        let concat98_out1 = burn::tensor::Tensor::cat(
            [neg22_out1, slice102_out1].into(),
            3,
        );
        let mul93_out1 = concat98_out1.mul(cast119_out1);
        let add75_out1 = mul92_out1.add(mul93_out1);
        let shape96_out1: [i64; 4] = {
            let axes = &add74_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let slice103_out1: [i64; 1] = shape96_out1[3..4].try_into().unwrap();
        let cast122_out1 = {
            let shape_array = slice103_out1 as [i64; 1usize];
            let float_array: [f64; 1usize] = shape_array.map(|x| x as f64);
            Tensor::<
                1,
            >::from_data(
                TensorData::from(float_array),
                (&self.device, burn::tensor::DType::F32),
            )
        };
        let sqrt31_out1 = cast122_out1.sqrt();
        let constant1287_out1 = self.constant1287.val();
        let div45_out1 = constant1287_out1.div(sqrt31_out1);
        let cast123_out1 = div45_out1;
        let transpose32_out1 = add75_out1.permute([0, 1, 3, 2]);
        let sqrt32_out1 = cast123_out1.clone().sqrt();
        let mul94_out1 = add74_out1
            .mul((sqrt32_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let sqrt33_out1 = cast123_out1.sqrt();
        let mul95_out1 = transpose32_out1
            .mul((sqrt33_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let matmul63_out1 = mul94_out1.matmul(mul95_out1);
        let constant1289_out1 = self.constant1289.val();
        let constant1288_out1 = self.constant1288.val();
        let where27_out1 = constant1289_out1
            .unsqueeze_dims(&[0isize, 1isize, 2isize])
            .mask_where(
                less4_out1,
                constant1288_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let add76_out1 = matmul63_out1.add(where27_out1);
        let softmax11_out1 = burn::tensor::activation::softmax(add76_out1, 3);
        let isnan11_out1 = softmax11_out1.clone().is_nan();
        let constant1290_out1 = self.constant1290.val();
        let where28_out1 = softmax11_out1
            .mask_where(
                isnan11_out1,
                constant1290_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let matmul64_out1 = where28_out1.matmul(squeeze33_out1);
        let transpose33_out1 = matmul64_out1.permute([0, 2, 1, 3]);
        let unsqueeze226_out1 = [gather72_out1 as i64];
        let unsqueeze227_out1 = [gather73_out1 as i64];
        let constant1293_out1: [i64; 1] = [512i64];
        let concat99_out1: [i64; 3usize] = [
            &unsqueeze226_out1[..],
            &unsqueeze227_out1[..],
            &constant1293_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape76_out1 = transpose33_out1.reshape(concat99_out1);
        let linear43_out1 = self.linear43.forward(reshape76_out1);
        let add77_out1 = add73_out1.add(linear43_out1);
        add77_out1
    }
}
#[derive(Module, Debug)]
pub struct Submodule13 {
    layernormalization23: LayerNorm,
    linear44: Linear,
    constant1294: burn::module::Param<Tensor<1>>,
    constant1295: burn::module::Param<Tensor<1>>,
    constant1296: burn::module::Param<Tensor<1>>,
    linear45: Linear,
    layernormalization24: LayerNorm,
    linear46: Linear,
    constant1364: burn::module::Param<Tensor<1>>,
    constant1366: burn::module::Param<Tensor<1>>,
    constant1365: burn::module::Param<Tensor<1>>,
    constant1367: burn::module::Param<Tensor<1>>,
    linear47: Linear,
    #[module(skip)]
    device: Device,
}
impl Submodule13 {
    #[allow(unused_variables)]
    pub fn new(device: &Device) -> Self {
        let layernormalization23 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear44 = LinearConfig::new(512, 2048).with_bias(true).init(device);
        let constant1294: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1.4142135381698608f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1295: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1296: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([0.5f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let linear45 = LinearConfig::new(2048, 512).with_bias(true).init(device);
        let layernormalization24 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear46 = LinearConfig::new(512, 1536).with_bias(false).init(device);
        let constant1364: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1366: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1365: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1367: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let linear47 = LinearConfig::new(512, 512).with_bias(true).init(device);
        Self {
            layernormalization23,
            linear44,
            constant1294,
            constant1295,
            constant1296,
            linear45,
            layernormalization24,
            linear46,
            constant1364,
            constant1366,
            constant1365,
            constant1367,
            linear47,
            device: device.clone(),
        }
    }
    #[allow(clippy::let_and_return, clippy::approx_constant)]
    pub fn forward(
        &self,
        add77_out1: Tensor<3>,
        constant461_out1: Tensor<2>,
        constant474_out1: Tensor<2>,
        less4_out1: Tensor<4, Bool>,
    ) -> Tensor<3> {
        let layernormalization23_out1 = {
            let dtype = add77_out1.clone().dtype();
            self.layernormalization23
                .forward(add77_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let linear44_out1 = self.linear44.forward(layernormalization23_out1);
        let constant1294_out1 = self.constant1294.val();
        let div46_out1 = linear44_out1
            .clone()
            .div((constant1294_out1).unsqueeze_dims(&[0isize, 1isize]));
        let erf11_out1 = div46_out1.erf();
        let constant1295_out1 = self.constant1295.val();
        let add78_out1 = erf11_out1
            .add((constant1295_out1).unsqueeze_dims(&[0isize, 1isize]));
        let mul96_out1 = linear44_out1.mul(add78_out1);
        let constant1296_out1 = self.constant1296.val();
        let mul97_out1 = mul96_out1
            .mul((constant1296_out1).unsqueeze_dims(&[0isize, 1isize]));
        let linear45_out1 = self.linear45.forward(mul97_out1);
        let add79_out1 = add77_out1.add(linear45_out1);
        let layernormalization24_out1 = {
            let dtype = add79_out1.clone().dtype();
            self.layernormalization24
                .forward(add79_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let shape97_out1: [i64; 3] = {
            let axes = &layernormalization24_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather78_out1 = shape97_out1[0] as i64;
        let shape98_out1: [i64; 3] = {
            let axes = &layernormalization24_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather79_out1 = shape98_out1[1] as i64;
        let linear46_out1 = self.linear46.forward(layernormalization24_out1);
        let unsqueeze228_out1 = [gather78_out1 as i64];
        let unsqueeze229_out1 = [gather79_out1 as i64];
        let constant1303_out1: [i64; 1] = [64i64];
        let constant1301_out1: [i64; 1] = [3i64];
        let constant1302_out1: [i64; 1] = [8i64];
        let concat100_out1: [i64; 5usize] = [
            &unsqueeze228_out1[..],
            &unsqueeze229_out1[..],
            &constant1301_out1[..],
            &constant1302_out1[..],
            &constant1303_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape77_out1 = linear46_out1.reshape(concat100_out1);
        let transpose34_out1 = reshape77_out1.permute([2, 0, 3, 1, 4]);
        let split_tensors = transpose34_out1.split_with_sizes([1, 1, 1].into(), 0);
        let [split12_out1, split12_out2, split12_out3] = split_tensors
            .try_into()
            .unwrap();
        let squeeze34_out1 = split12_out1.squeeze_dims::<4>(&[0]);
        let squeeze35_out1 = split12_out2.squeeze_dims::<4>(&[0]);
        let squeeze36_out1 = split12_out3.squeeze_dims::<4>(&[0]);
        let shape99_out1: [i64; 4] = {
            let axes = &squeeze34_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather80_out1 = shape99_out1[2] as i64;
        let shape100_out1: [i64; 4] = {
            let axes = &squeeze35_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather81_out1 = shape100_out1[2] as i64;
        let sub14_out1 = gather81_out1 - gather80_out1;
        let unsqueeze230_out1 = [gather81_out1 as i64];
        let slice104_out1 = constant461_out1
            .clone()
            .slice(s![0..unsqueeze230_out1[0], ..]);
        let unsqueeze231_out1 = [gather81_out1 as i64];
        let constant1317_out1: [i64; 1] = [64i64];
        let constant1314_out1: [i64; 1] = [1i64];
        let constant1315_out1: [i64; 1] = [1i64];
        let concat101_out1: [i64; 4usize] = [
            &constant1314_out1[..],
            &constant1315_out1[..],
            &unsqueeze231_out1[..],
            &constant1317_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze232_out1 = [gather81_out1 as i64];
        let constant1321_out1: [i64; 1] = [64i64];
        let constant1318_out1: [i64; 1] = [1i64];
        let constant1319_out1: [i64; 1] = [1i64];
        let concat102_out1: [i64; 4usize] = [
            &constant1318_out1[..],
            &constant1319_out1[..],
            &unsqueeze232_out1[..],
            &constant1321_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape78_out1 = slice104_out1.reshape(concat101_out1);
        let unsqueeze233_out1 = [gather81_out1 as i64];
        let slice105_out1 = constant474_out1
            .clone()
            .slice(s![0..unsqueeze233_out1[0], ..]);
        let reshape79_out1 = slice105_out1.reshape(concat102_out1);
        let unsqueeze234_out1 = [sub14_out1 as i64];
        let unsqueeze235_out1 = [gather81_out1 as i64];
        let slice106_out1 = constant461_out1
            .slice(s![unsqueeze234_out1[0]..unsqueeze235_out1[0], ..]);
        let unsqueeze236_out1 = [gather80_out1 as i64];
        let constant1333_out1: [i64; 1] = [64i64];
        let constant1330_out1: [i64; 1] = [1i64];
        let constant1331_out1: [i64; 1] = [1i64];
        let concat103_out1: [i64; 4usize] = [
            &constant1330_out1[..],
            &constant1331_out1[..],
            &unsqueeze236_out1[..],
            &constant1333_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze237_out1 = [gather80_out1 as i64];
        let constant1337_out1: [i64; 1] = [64i64];
        let constant1334_out1: [i64; 1] = [1i64];
        let constant1335_out1: [i64; 1] = [1i64];
        let concat104_out1: [i64; 4usize] = [
            &constant1334_out1[..],
            &constant1335_out1[..],
            &unsqueeze237_out1[..],
            &constant1337_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape80_out1 = slice106_out1.reshape(concat103_out1);
        let unsqueeze238_out1 = [sub14_out1 as i64];
        let unsqueeze239_out1 = [gather81_out1 as i64];
        let slice107_out1 = constant474_out1
            .slice(s![unsqueeze238_out1[0]..unsqueeze239_out1[0], ..]);
        let reshape81_out1 = slice107_out1.reshape(concat104_out1);
        let cast124_out1 = reshape80_out1;
        let cast125_out1 = reshape81_out1;
        let mul98_out1 = squeeze34_out1.clone().mul(cast124_out1);
        let shape101_out1: [i64; 4] = {
            let axes = &squeeze34_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather82_out1 = shape101_out1[3] as i64;
        let constant1343_out1 = 2i64;
        let div47_out1 = gather82_out1 / constant1343_out1;
        let cast126_out1 = div47_out1;
        let cast127_out1 = cast126_out1;
        let unsqueeze240_out1 = [cast127_out1 as i64];
        let slice108_out1 = squeeze34_out1
            .clone()
            .slice(s![.., .., .., unsqueeze240_out1[0]..9223372036854775807]);
        let neg23_out1 = slice108_out1.neg();
        let unsqueeze241_out1 = [cast127_out1 as i64];
        let slice109_out1 = squeeze34_out1
            .slice(s![.., .., .., 0..unsqueeze241_out1[0]]);
        let concat105_out1 = burn::tensor::Tensor::cat(
            [neg23_out1, slice109_out1].into(),
            3,
        );
        let mul99_out1 = concat105_out1.mul(cast125_out1);
        let add80_out1 = mul98_out1.add(mul99_out1);
        let cast128_out1 = reshape78_out1;
        let cast129_out1 = reshape79_out1;
        let mul100_out1 = squeeze35_out1.clone().mul(cast128_out1);
        let shape102_out1: [i64; 4] = {
            let axes = &squeeze35_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather83_out1 = shape102_out1[3] as i64;
        let constant1353_out1 = 2i64;
        let div48_out1 = gather83_out1 / constant1353_out1;
        let cast130_out1 = div48_out1;
        let cast131_out1 = cast130_out1;
        let unsqueeze242_out1 = [cast131_out1 as i64];
        let slice110_out1 = squeeze35_out1
            .clone()
            .slice(s![.., .., .., unsqueeze242_out1[0]..9223372036854775807]);
        let neg24_out1 = slice110_out1.neg();
        let unsqueeze243_out1 = [cast131_out1 as i64];
        let slice111_out1 = squeeze35_out1
            .slice(s![.., .., .., 0..unsqueeze243_out1[0]]);
        let concat106_out1 = burn::tensor::Tensor::cat(
            [neg24_out1, slice111_out1].into(),
            3,
        );
        let mul101_out1 = concat106_out1.mul(cast129_out1);
        let add81_out1 = mul100_out1.add(mul101_out1);
        let shape103_out1: [i64; 4] = {
            let axes = &add80_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let slice112_out1: [i64; 1] = shape103_out1[3..4].try_into().unwrap();
        let cast132_out1 = {
            let shape_array = slice112_out1 as [i64; 1usize];
            let float_array: [f64; 1usize] = shape_array.map(|x| x as f64);
            Tensor::<
                1,
            >::from_data(
                TensorData::from(float_array),
                (&self.device, burn::tensor::DType::F32),
            )
        };
        let sqrt34_out1 = cast132_out1.sqrt();
        let constant1364_out1 = self.constant1364.val();
        let div49_out1 = constant1364_out1.div(sqrt34_out1);
        let cast133_out1 = div49_out1;
        let transpose35_out1 = add81_out1.permute([0, 1, 3, 2]);
        let sqrt35_out1 = cast133_out1.clone().sqrt();
        let mul102_out1 = add80_out1
            .mul((sqrt35_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let sqrt36_out1 = cast133_out1.sqrt();
        let mul103_out1 = transpose35_out1
            .mul((sqrt36_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let matmul69_out1 = mul102_out1.matmul(mul103_out1);
        let constant1366_out1 = self.constant1366.val();
        let constant1365_out1 = self.constant1365.val();
        let where29_out1 = constant1366_out1
            .unsqueeze_dims(&[0isize, 1isize, 2isize])
            .mask_where(
                less4_out1,
                constant1365_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let add82_out1 = matmul69_out1.add(where29_out1);
        let softmax12_out1 = burn::tensor::activation::softmax(add82_out1, 3);
        let isnan12_out1 = softmax12_out1.clone().is_nan();
        let constant1367_out1 = self.constant1367.val();
        let where30_out1 = softmax12_out1
            .mask_where(
                isnan12_out1,
                constant1367_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let matmul70_out1 = where30_out1.matmul(squeeze36_out1);
        let transpose36_out1 = matmul70_out1.permute([0, 2, 1, 3]);
        let unsqueeze244_out1 = [gather78_out1 as i64];
        let unsqueeze245_out1 = [gather79_out1 as i64];
        let constant1370_out1: [i64; 1] = [512i64];
        let concat107_out1: [i64; 3usize] = [
            &unsqueeze244_out1[..],
            &unsqueeze245_out1[..],
            &constant1370_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape82_out1 = transpose36_out1.reshape(concat107_out1);
        let linear47_out1 = self.linear47.forward(reshape82_out1);
        let add83_out1 = add79_out1.add(linear47_out1);
        add83_out1
    }
}
#[derive(Module, Debug)]
pub struct Submodule14 {
    layernormalization25: LayerNorm,
    linear48: Linear,
    constant1371: burn::module::Param<Tensor<1>>,
    constant1372: burn::module::Param<Tensor<1>>,
    constant1373: burn::module::Param<Tensor<1>>,
    linear49: Linear,
    layernormalization26: LayerNorm,
    linear50: Linear,
    constant1441: burn::module::Param<Tensor<1>>,
    constant1443: burn::module::Param<Tensor<1>>,
    constant1442: burn::module::Param<Tensor<1>>,
    constant1444: burn::module::Param<Tensor<1>>,
    linear51: Linear,
    #[module(skip)]
    device: Device,
}
impl Submodule14 {
    #[allow(unused_variables)]
    pub fn new(device: &Device) -> Self {
        let layernormalization25 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear48 = LinearConfig::new(512, 2048).with_bias(true).init(device);
        let constant1371: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1.4142135381698608f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1372: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1373: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([0.5f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let linear49 = LinearConfig::new(2048, 512).with_bias(true).init(device);
        let layernormalization26 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear50 = LinearConfig::new(512, 1536).with_bias(false).init(device);
        let constant1441: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1443: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1442: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1444: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let linear51 = LinearConfig::new(512, 512).with_bias(true).init(device);
        Self {
            layernormalization25,
            linear48,
            constant1371,
            constant1372,
            constant1373,
            linear49,
            layernormalization26,
            linear50,
            constant1441,
            constant1443,
            constant1442,
            constant1444,
            linear51,
            device: device.clone(),
        }
    }
    #[allow(clippy::let_and_return, clippy::approx_constant)]
    pub fn forward(
        &self,
        add83_out1: Tensor<3>,
        constant461_out1: Tensor<2>,
        constant474_out1: Tensor<2>,
        less4_out1: Tensor<4, Bool>,
    ) -> Tensor<3> {
        let layernormalization25_out1 = {
            let dtype = add83_out1.clone().dtype();
            self.layernormalization25
                .forward(add83_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let linear48_out1 = self.linear48.forward(layernormalization25_out1);
        let constant1371_out1 = self.constant1371.val();
        let div50_out1 = linear48_out1
            .clone()
            .div((constant1371_out1).unsqueeze_dims(&[0isize, 1isize]));
        let erf12_out1 = div50_out1.erf();
        let constant1372_out1 = self.constant1372.val();
        let add84_out1 = erf12_out1
            .add((constant1372_out1).unsqueeze_dims(&[0isize, 1isize]));
        let mul104_out1 = linear48_out1.mul(add84_out1);
        let constant1373_out1 = self.constant1373.val();
        let mul105_out1 = mul104_out1
            .mul((constant1373_out1).unsqueeze_dims(&[0isize, 1isize]));
        let linear49_out1 = self.linear49.forward(mul105_out1);
        let add85_out1 = add83_out1.add(linear49_out1);
        let layernormalization26_out1 = {
            let dtype = add85_out1.clone().dtype();
            self.layernormalization26
                .forward(add85_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let shape104_out1: [i64; 3] = {
            let axes = &layernormalization26_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather84_out1 = shape104_out1[0] as i64;
        let shape105_out1: [i64; 3] = {
            let axes = &layernormalization26_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather85_out1 = shape105_out1[1] as i64;
        let linear50_out1 = self.linear50.forward(layernormalization26_out1);
        let unsqueeze246_out1 = [gather84_out1 as i64];
        let unsqueeze247_out1 = [gather85_out1 as i64];
        let constant1380_out1: [i64; 1] = [64i64];
        let constant1378_out1: [i64; 1] = [3i64];
        let constant1379_out1: [i64; 1] = [8i64];
        let concat108_out1: [i64; 5usize] = [
            &unsqueeze246_out1[..],
            &unsqueeze247_out1[..],
            &constant1378_out1[..],
            &constant1379_out1[..],
            &constant1380_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape83_out1 = linear50_out1.reshape(concat108_out1);
        let transpose37_out1 = reshape83_out1.permute([2, 0, 3, 1, 4]);
        let split_tensors = transpose37_out1.split_with_sizes([1, 1, 1].into(), 0);
        let [split13_out1, split13_out2, split13_out3] = split_tensors
            .try_into()
            .unwrap();
        let squeeze37_out1 = split13_out1.squeeze_dims::<4>(&[0]);
        let squeeze38_out1 = split13_out2.squeeze_dims::<4>(&[0]);
        let squeeze39_out1 = split13_out3.squeeze_dims::<4>(&[0]);
        let shape106_out1: [i64; 4] = {
            let axes = &squeeze37_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather86_out1 = shape106_out1[2] as i64;
        let shape107_out1: [i64; 4] = {
            let axes = &squeeze38_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather87_out1 = shape107_out1[2] as i64;
        let sub15_out1 = gather87_out1 - gather86_out1;
        let unsqueeze248_out1 = [gather87_out1 as i64];
        let slice113_out1 = constant461_out1
            .clone()
            .slice(s![0..unsqueeze248_out1[0], ..]);
        let unsqueeze249_out1 = [gather87_out1 as i64];
        let constant1394_out1: [i64; 1] = [64i64];
        let constant1391_out1: [i64; 1] = [1i64];
        let constant1392_out1: [i64; 1] = [1i64];
        let concat109_out1: [i64; 4usize] = [
            &constant1391_out1[..],
            &constant1392_out1[..],
            &unsqueeze249_out1[..],
            &constant1394_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze250_out1 = [gather87_out1 as i64];
        let constant1398_out1: [i64; 1] = [64i64];
        let constant1395_out1: [i64; 1] = [1i64];
        let constant1396_out1: [i64; 1] = [1i64];
        let concat110_out1: [i64; 4usize] = [
            &constant1395_out1[..],
            &constant1396_out1[..],
            &unsqueeze250_out1[..],
            &constant1398_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape84_out1 = slice113_out1.reshape(concat109_out1);
        let unsqueeze251_out1 = [gather87_out1 as i64];
        let slice114_out1 = constant474_out1
            .clone()
            .slice(s![0..unsqueeze251_out1[0], ..]);
        let reshape85_out1 = slice114_out1.reshape(concat110_out1);
        let unsqueeze252_out1 = [sub15_out1 as i64];
        let unsqueeze253_out1 = [gather87_out1 as i64];
        let slice115_out1 = constant461_out1
            .slice(s![unsqueeze252_out1[0]..unsqueeze253_out1[0], ..]);
        let unsqueeze254_out1 = [gather86_out1 as i64];
        let constant1410_out1: [i64; 1] = [64i64];
        let constant1407_out1: [i64; 1] = [1i64];
        let constant1408_out1: [i64; 1] = [1i64];
        let concat111_out1: [i64; 4usize] = [
            &constant1407_out1[..],
            &constant1408_out1[..],
            &unsqueeze254_out1[..],
            &constant1410_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze255_out1 = [gather86_out1 as i64];
        let constant1414_out1: [i64; 1] = [64i64];
        let constant1411_out1: [i64; 1] = [1i64];
        let constant1412_out1: [i64; 1] = [1i64];
        let concat112_out1: [i64; 4usize] = [
            &constant1411_out1[..],
            &constant1412_out1[..],
            &unsqueeze255_out1[..],
            &constant1414_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape86_out1 = slice115_out1.reshape(concat111_out1);
        let unsqueeze256_out1 = [sub15_out1 as i64];
        let unsqueeze257_out1 = [gather87_out1 as i64];
        let slice116_out1 = constant474_out1
            .slice(s![unsqueeze256_out1[0]..unsqueeze257_out1[0], ..]);
        let reshape87_out1 = slice116_out1.reshape(concat112_out1);
        let cast134_out1 = reshape86_out1;
        let cast135_out1 = reshape87_out1;
        let mul106_out1 = squeeze37_out1.clone().mul(cast134_out1);
        let shape108_out1: [i64; 4] = {
            let axes = &squeeze37_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather88_out1 = shape108_out1[3] as i64;
        let constant1420_out1 = 2i64;
        let div51_out1 = gather88_out1 / constant1420_out1;
        let cast136_out1 = div51_out1;
        let cast137_out1 = cast136_out1;
        let unsqueeze258_out1 = [cast137_out1 as i64];
        let slice117_out1 = squeeze37_out1
            .clone()
            .slice(s![.., .., .., unsqueeze258_out1[0]..9223372036854775807]);
        let neg25_out1 = slice117_out1.neg();
        let unsqueeze259_out1 = [cast137_out1 as i64];
        let slice118_out1 = squeeze37_out1
            .slice(s![.., .., .., 0..unsqueeze259_out1[0]]);
        let concat113_out1 = burn::tensor::Tensor::cat(
            [neg25_out1, slice118_out1].into(),
            3,
        );
        let mul107_out1 = concat113_out1.mul(cast135_out1);
        let add86_out1 = mul106_out1.add(mul107_out1);
        let cast138_out1 = reshape84_out1;
        let cast139_out1 = reshape85_out1;
        let mul108_out1 = squeeze38_out1.clone().mul(cast138_out1);
        let shape109_out1: [i64; 4] = {
            let axes = &squeeze38_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather89_out1 = shape109_out1[3] as i64;
        let constant1430_out1 = 2i64;
        let div52_out1 = gather89_out1 / constant1430_out1;
        let cast140_out1 = div52_out1;
        let cast141_out1 = cast140_out1;
        let unsqueeze260_out1 = [cast141_out1 as i64];
        let slice119_out1 = squeeze38_out1
            .clone()
            .slice(s![.., .., .., unsqueeze260_out1[0]..9223372036854775807]);
        let neg26_out1 = slice119_out1.neg();
        let unsqueeze261_out1 = [cast141_out1 as i64];
        let slice120_out1 = squeeze38_out1
            .slice(s![.., .., .., 0..unsqueeze261_out1[0]]);
        let concat114_out1 = burn::tensor::Tensor::cat(
            [neg26_out1, slice120_out1].into(),
            3,
        );
        let mul109_out1 = concat114_out1.mul(cast139_out1);
        let add87_out1 = mul108_out1.add(mul109_out1);
        let shape110_out1: [i64; 4] = {
            let axes = &add86_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let slice121_out1: [i64; 1] = shape110_out1[3..4].try_into().unwrap();
        let cast142_out1 = {
            let shape_array = slice121_out1 as [i64; 1usize];
            let float_array: [f64; 1usize] = shape_array.map(|x| x as f64);
            Tensor::<
                1,
            >::from_data(
                TensorData::from(float_array),
                (&self.device, burn::tensor::DType::F32),
            )
        };
        let sqrt37_out1 = cast142_out1.sqrt();
        let constant1441_out1 = self.constant1441.val();
        let div53_out1 = constant1441_out1.div(sqrt37_out1);
        let cast143_out1 = div53_out1;
        let transpose38_out1 = add87_out1.permute([0, 1, 3, 2]);
        let sqrt38_out1 = cast143_out1.clone().sqrt();
        let mul110_out1 = add86_out1
            .mul((sqrt38_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let sqrt39_out1 = cast143_out1.sqrt();
        let mul111_out1 = transpose38_out1
            .mul((sqrt39_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let matmul75_out1 = mul110_out1.matmul(mul111_out1);
        let constant1443_out1 = self.constant1443.val();
        let constant1442_out1 = self.constant1442.val();
        let where31_out1 = constant1443_out1
            .unsqueeze_dims(&[0isize, 1isize, 2isize])
            .mask_where(
                less4_out1,
                constant1442_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let add88_out1 = matmul75_out1.add(where31_out1);
        let softmax13_out1 = burn::tensor::activation::softmax(add88_out1, 3);
        let isnan13_out1 = softmax13_out1.clone().is_nan();
        let constant1444_out1 = self.constant1444.val();
        let where32_out1 = softmax13_out1
            .mask_where(
                isnan13_out1,
                constant1444_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let matmul76_out1 = where32_out1.matmul(squeeze39_out1);
        let transpose39_out1 = matmul76_out1.permute([0, 2, 1, 3]);
        let unsqueeze262_out1 = [gather84_out1 as i64];
        let unsqueeze263_out1 = [gather85_out1 as i64];
        let constant1447_out1: [i64; 1] = [512i64];
        let concat115_out1: [i64; 3usize] = [
            &unsqueeze262_out1[..],
            &unsqueeze263_out1[..],
            &constant1447_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape88_out1 = transpose39_out1.reshape(concat115_out1);
        let linear51_out1 = self.linear51.forward(reshape88_out1);
        let add89_out1 = add85_out1.add(linear51_out1);
        add89_out1
    }
}
#[derive(Module, Debug)]
pub struct Submodule15 {
    layernormalization27: LayerNorm,
    linear52: Linear,
    constant1448: burn::module::Param<Tensor<1>>,
    constant1449: burn::module::Param<Tensor<1>>,
    constant1450: burn::module::Param<Tensor<1>>,
    linear53: Linear,
    layernormalization28: LayerNorm,
    linear54: Linear,
    constant1518: burn::module::Param<Tensor<1>>,
    constant1520: burn::module::Param<Tensor<1>>,
    constant1519: burn::module::Param<Tensor<1>>,
    constant1521: burn::module::Param<Tensor<1>>,
    linear55: Linear,
    #[module(skip)]
    device: Device,
}
impl Submodule15 {
    #[allow(unused_variables)]
    pub fn new(device: &Device) -> Self {
        let layernormalization27 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear52 = LinearConfig::new(512, 2048).with_bias(true).init(device);
        let constant1448: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1.4142135381698608f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1449: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1450: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([0.5f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let linear53 = LinearConfig::new(2048, 512).with_bias(true).init(device);
        let layernormalization28 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear54 = LinearConfig::new(512, 1536).with_bias(false).init(device);
        let constant1518: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1520: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1519: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1521: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let linear55 = LinearConfig::new(512, 512).with_bias(true).init(device);
        Self {
            layernormalization27,
            linear52,
            constant1448,
            constant1449,
            constant1450,
            linear53,
            layernormalization28,
            linear54,
            constant1518,
            constant1520,
            constant1519,
            constant1521,
            linear55,
            device: device.clone(),
        }
    }
    #[allow(clippy::let_and_return, clippy::approx_constant)]
    pub fn forward(
        &self,
        add89_out1: Tensor<3>,
        constant461_out1: Tensor<2>,
        constant474_out1: Tensor<2>,
        less4_out1: Tensor<4, Bool>,
    ) -> Tensor<3> {
        let layernormalization27_out1 = {
            let dtype = add89_out1.clone().dtype();
            self.layernormalization27
                .forward(add89_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let linear52_out1 = self.linear52.forward(layernormalization27_out1);
        let constant1448_out1 = self.constant1448.val();
        let div54_out1 = linear52_out1
            .clone()
            .div((constant1448_out1).unsqueeze_dims(&[0isize, 1isize]));
        let erf13_out1 = div54_out1.erf();
        let constant1449_out1 = self.constant1449.val();
        let add90_out1 = erf13_out1
            .add((constant1449_out1).unsqueeze_dims(&[0isize, 1isize]));
        let mul112_out1 = linear52_out1.mul(add90_out1);
        let constant1450_out1 = self.constant1450.val();
        let mul113_out1 = mul112_out1
            .mul((constant1450_out1).unsqueeze_dims(&[0isize, 1isize]));
        let linear53_out1 = self.linear53.forward(mul113_out1);
        let add91_out1 = add89_out1.add(linear53_out1);
        let layernormalization28_out1 = {
            let dtype = add91_out1.clone().dtype();
            self.layernormalization28
                .forward(add91_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let shape111_out1: [i64; 3] = {
            let axes = &layernormalization28_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather90_out1 = shape111_out1[0] as i64;
        let shape112_out1: [i64; 3] = {
            let axes = &layernormalization28_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather91_out1 = shape112_out1[1] as i64;
        let linear54_out1 = self.linear54.forward(layernormalization28_out1);
        let unsqueeze264_out1 = [gather90_out1 as i64];
        let unsqueeze265_out1 = [gather91_out1 as i64];
        let constant1457_out1: [i64; 1] = [64i64];
        let constant1455_out1: [i64; 1] = [3i64];
        let constant1456_out1: [i64; 1] = [8i64];
        let concat116_out1: [i64; 5usize] = [
            &unsqueeze264_out1[..],
            &unsqueeze265_out1[..],
            &constant1455_out1[..],
            &constant1456_out1[..],
            &constant1457_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape89_out1 = linear54_out1.reshape(concat116_out1);
        let transpose40_out1 = reshape89_out1.permute([2, 0, 3, 1, 4]);
        let split_tensors = transpose40_out1.split_with_sizes([1, 1, 1].into(), 0);
        let [split14_out1, split14_out2, split14_out3] = split_tensors
            .try_into()
            .unwrap();
        let squeeze40_out1 = split14_out1.squeeze_dims::<4>(&[0]);
        let squeeze41_out1 = split14_out2.squeeze_dims::<4>(&[0]);
        let squeeze42_out1 = split14_out3.squeeze_dims::<4>(&[0]);
        let shape113_out1: [i64; 4] = {
            let axes = &squeeze40_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather92_out1 = shape113_out1[2] as i64;
        let shape114_out1: [i64; 4] = {
            let axes = &squeeze41_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather93_out1 = shape114_out1[2] as i64;
        let sub16_out1 = gather93_out1 - gather92_out1;
        let unsqueeze266_out1 = [gather93_out1 as i64];
        let slice122_out1 = constant461_out1
            .clone()
            .slice(s![0..unsqueeze266_out1[0], ..]);
        let unsqueeze267_out1 = [gather93_out1 as i64];
        let constant1471_out1: [i64; 1] = [64i64];
        let constant1468_out1: [i64; 1] = [1i64];
        let constant1469_out1: [i64; 1] = [1i64];
        let concat117_out1: [i64; 4usize] = [
            &constant1468_out1[..],
            &constant1469_out1[..],
            &unsqueeze267_out1[..],
            &constant1471_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze268_out1 = [gather93_out1 as i64];
        let constant1475_out1: [i64; 1] = [64i64];
        let constant1472_out1: [i64; 1] = [1i64];
        let constant1473_out1: [i64; 1] = [1i64];
        let concat118_out1: [i64; 4usize] = [
            &constant1472_out1[..],
            &constant1473_out1[..],
            &unsqueeze268_out1[..],
            &constant1475_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape90_out1 = slice122_out1.reshape(concat117_out1);
        let unsqueeze269_out1 = [gather93_out1 as i64];
        let slice123_out1 = constant474_out1
            .clone()
            .slice(s![0..unsqueeze269_out1[0], ..]);
        let reshape91_out1 = slice123_out1.reshape(concat118_out1);
        let unsqueeze270_out1 = [sub16_out1 as i64];
        let unsqueeze271_out1 = [gather93_out1 as i64];
        let slice124_out1 = constant461_out1
            .slice(s![unsqueeze270_out1[0]..unsqueeze271_out1[0], ..]);
        let unsqueeze272_out1 = [gather92_out1 as i64];
        let constant1487_out1: [i64; 1] = [64i64];
        let constant1484_out1: [i64; 1] = [1i64];
        let constant1485_out1: [i64; 1] = [1i64];
        let concat119_out1: [i64; 4usize] = [
            &constant1484_out1[..],
            &constant1485_out1[..],
            &unsqueeze272_out1[..],
            &constant1487_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze273_out1 = [gather92_out1 as i64];
        let constant1491_out1: [i64; 1] = [64i64];
        let constant1488_out1: [i64; 1] = [1i64];
        let constant1489_out1: [i64; 1] = [1i64];
        let concat120_out1: [i64; 4usize] = [
            &constant1488_out1[..],
            &constant1489_out1[..],
            &unsqueeze273_out1[..],
            &constant1491_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape92_out1 = slice124_out1.reshape(concat119_out1);
        let unsqueeze274_out1 = [sub16_out1 as i64];
        let unsqueeze275_out1 = [gather93_out1 as i64];
        let slice125_out1 = constant474_out1
            .slice(s![unsqueeze274_out1[0]..unsqueeze275_out1[0], ..]);
        let reshape93_out1 = slice125_out1.reshape(concat120_out1);
        let cast144_out1 = reshape92_out1;
        let cast145_out1 = reshape93_out1;
        let mul114_out1 = squeeze40_out1.clone().mul(cast144_out1);
        let shape115_out1: [i64; 4] = {
            let axes = &squeeze40_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather94_out1 = shape115_out1[3] as i64;
        let constant1497_out1 = 2i64;
        let div55_out1 = gather94_out1 / constant1497_out1;
        let cast146_out1 = div55_out1;
        let cast147_out1 = cast146_out1;
        let unsqueeze276_out1 = [cast147_out1 as i64];
        let slice126_out1 = squeeze40_out1
            .clone()
            .slice(s![.., .., .., unsqueeze276_out1[0]..9223372036854775807]);
        let neg27_out1 = slice126_out1.neg();
        let unsqueeze277_out1 = [cast147_out1 as i64];
        let slice127_out1 = squeeze40_out1
            .slice(s![.., .., .., 0..unsqueeze277_out1[0]]);
        let concat121_out1 = burn::tensor::Tensor::cat(
            [neg27_out1, slice127_out1].into(),
            3,
        );
        let mul115_out1 = concat121_out1.mul(cast145_out1);
        let add92_out1 = mul114_out1.add(mul115_out1);
        let cast148_out1 = reshape90_out1;
        let cast149_out1 = reshape91_out1;
        let mul116_out1 = squeeze41_out1.clone().mul(cast148_out1);
        let shape116_out1: [i64; 4] = {
            let axes = &squeeze41_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather95_out1 = shape116_out1[3] as i64;
        let constant1507_out1 = 2i64;
        let div56_out1 = gather95_out1 / constant1507_out1;
        let cast150_out1 = div56_out1;
        let cast151_out1 = cast150_out1;
        let unsqueeze278_out1 = [cast151_out1 as i64];
        let slice128_out1 = squeeze41_out1
            .clone()
            .slice(s![.., .., .., unsqueeze278_out1[0]..9223372036854775807]);
        let neg28_out1 = slice128_out1.neg();
        let unsqueeze279_out1 = [cast151_out1 as i64];
        let slice129_out1 = squeeze41_out1
            .slice(s![.., .., .., 0..unsqueeze279_out1[0]]);
        let concat122_out1 = burn::tensor::Tensor::cat(
            [neg28_out1, slice129_out1].into(),
            3,
        );
        let mul117_out1 = concat122_out1.mul(cast149_out1);
        let add93_out1 = mul116_out1.add(mul117_out1);
        let shape117_out1: [i64; 4] = {
            let axes = &add92_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let slice130_out1: [i64; 1] = shape117_out1[3..4].try_into().unwrap();
        let cast152_out1 = {
            let shape_array = slice130_out1 as [i64; 1usize];
            let float_array: [f64; 1usize] = shape_array.map(|x| x as f64);
            Tensor::<
                1,
            >::from_data(
                TensorData::from(float_array),
                (&self.device, burn::tensor::DType::F32),
            )
        };
        let sqrt40_out1 = cast152_out1.sqrt();
        let constant1518_out1 = self.constant1518.val();
        let div57_out1 = constant1518_out1.div(sqrt40_out1);
        let cast153_out1 = div57_out1;
        let transpose41_out1 = add93_out1.permute([0, 1, 3, 2]);
        let sqrt41_out1 = cast153_out1.clone().sqrt();
        let mul118_out1 = add92_out1
            .mul((sqrt41_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let sqrt42_out1 = cast153_out1.sqrt();
        let mul119_out1 = transpose41_out1
            .mul((sqrt42_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let matmul81_out1 = mul118_out1.matmul(mul119_out1);
        let constant1520_out1 = self.constant1520.val();
        let constant1519_out1 = self.constant1519.val();
        let where33_out1 = constant1520_out1
            .unsqueeze_dims(&[0isize, 1isize, 2isize])
            .mask_where(
                less4_out1,
                constant1519_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let add94_out1 = matmul81_out1.add(where33_out1);
        let softmax14_out1 = burn::tensor::activation::softmax(add94_out1, 3);
        let isnan14_out1 = softmax14_out1.clone().is_nan();
        let constant1521_out1 = self.constant1521.val();
        let where34_out1 = softmax14_out1
            .mask_where(
                isnan14_out1,
                constant1521_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let matmul82_out1 = where34_out1.matmul(squeeze42_out1);
        let transpose42_out1 = matmul82_out1.permute([0, 2, 1, 3]);
        let unsqueeze280_out1 = [gather90_out1 as i64];
        let unsqueeze281_out1 = [gather91_out1 as i64];
        let constant1524_out1: [i64; 1] = [512i64];
        let concat123_out1: [i64; 3usize] = [
            &unsqueeze280_out1[..],
            &unsqueeze281_out1[..],
            &constant1524_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape94_out1 = transpose42_out1.reshape(concat123_out1);
        let linear55_out1 = self.linear55.forward(reshape94_out1);
        let add95_out1 = add91_out1.add(linear55_out1);
        add95_out1
    }
}
#[derive(Module, Debug)]
pub struct Submodule16 {
    layernormalization29: LayerNorm,
    linear56: Linear,
    constant1525: burn::module::Param<Tensor<1>>,
    constant1526: burn::module::Param<Tensor<1>>,
    constant1527: burn::module::Param<Tensor<1>>,
    linear57: Linear,
    layernormalization30: LayerNorm,
    linear58: Linear,
    constant1595: burn::module::Param<Tensor<1>>,
    constant1597: burn::module::Param<Tensor<1>>,
    constant1596: burn::module::Param<Tensor<1>>,
    constant1598: burn::module::Param<Tensor<1>>,
    linear59: Linear,
    #[module(skip)]
    device: Device,
}
impl Submodule16 {
    #[allow(unused_variables)]
    pub fn new(device: &Device) -> Self {
        let layernormalization29 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear56 = LinearConfig::new(512, 2048).with_bias(true).init(device);
        let constant1525: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1.4142135381698608f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1526: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1527: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([0.5f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let linear57 = LinearConfig::new(2048, 512).with_bias(true).init(device);
        let layernormalization30 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear58 = LinearConfig::new(512, 1536).with_bias(false).init(device);
        let constant1595: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1597: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1596: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1598: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let linear59 = LinearConfig::new(512, 512).with_bias(true).init(device);
        Self {
            layernormalization29,
            linear56,
            constant1525,
            constant1526,
            constant1527,
            linear57,
            layernormalization30,
            linear58,
            constant1595,
            constant1597,
            constant1596,
            constant1598,
            linear59,
            device: device.clone(),
        }
    }
    #[allow(clippy::let_and_return, clippy::approx_constant)]
    pub fn forward(
        &self,
        add95_out1: Tensor<3>,
        constant461_out1: Tensor<2>,
        constant474_out1: Tensor<2>,
        less4_out1: Tensor<4, Bool>,
    ) -> Tensor<3> {
        let layernormalization29_out1 = {
            let dtype = add95_out1.clone().dtype();
            self.layernormalization29
                .forward(add95_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let linear56_out1 = self.linear56.forward(layernormalization29_out1);
        let constant1525_out1 = self.constant1525.val();
        let div58_out1 = linear56_out1
            .clone()
            .div((constant1525_out1).unsqueeze_dims(&[0isize, 1isize]));
        let erf14_out1 = div58_out1.erf();
        let constant1526_out1 = self.constant1526.val();
        let add96_out1 = erf14_out1
            .add((constant1526_out1).unsqueeze_dims(&[0isize, 1isize]));
        let mul120_out1 = linear56_out1.mul(add96_out1);
        let constant1527_out1 = self.constant1527.val();
        let mul121_out1 = mul120_out1
            .mul((constant1527_out1).unsqueeze_dims(&[0isize, 1isize]));
        let linear57_out1 = self.linear57.forward(mul121_out1);
        let add97_out1 = add95_out1.add(linear57_out1);
        let layernormalization30_out1 = {
            let dtype = add97_out1.clone().dtype();
            self.layernormalization30
                .forward(add97_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let shape118_out1: [i64; 3] = {
            let axes = &layernormalization30_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather96_out1 = shape118_out1[0] as i64;
        let shape119_out1: [i64; 3] = {
            let axes = &layernormalization30_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather97_out1 = shape119_out1[1] as i64;
        let linear58_out1 = self.linear58.forward(layernormalization30_out1);
        let unsqueeze282_out1 = [gather96_out1 as i64];
        let unsqueeze283_out1 = [gather97_out1 as i64];
        let constant1534_out1: [i64; 1] = [64i64];
        let constant1532_out1: [i64; 1] = [3i64];
        let constant1533_out1: [i64; 1] = [8i64];
        let concat124_out1: [i64; 5usize] = [
            &unsqueeze282_out1[..],
            &unsqueeze283_out1[..],
            &constant1532_out1[..],
            &constant1533_out1[..],
            &constant1534_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape95_out1 = linear58_out1.reshape(concat124_out1);
        let transpose43_out1 = reshape95_out1.permute([2, 0, 3, 1, 4]);
        let split_tensors = transpose43_out1.split_with_sizes([1, 1, 1].into(), 0);
        let [split15_out1, split15_out2, split15_out3] = split_tensors
            .try_into()
            .unwrap();
        let squeeze43_out1 = split15_out1.squeeze_dims::<4>(&[0]);
        let squeeze44_out1 = split15_out2.squeeze_dims::<4>(&[0]);
        let squeeze45_out1 = split15_out3.squeeze_dims::<4>(&[0]);
        let shape120_out1: [i64; 4] = {
            let axes = &squeeze43_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather98_out1 = shape120_out1[2] as i64;
        let shape121_out1: [i64; 4] = {
            let axes = &squeeze44_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather99_out1 = shape121_out1[2] as i64;
        let sub17_out1 = gather99_out1 - gather98_out1;
        let unsqueeze284_out1 = [gather99_out1 as i64];
        let slice131_out1 = constant461_out1
            .clone()
            .slice(s![0..unsqueeze284_out1[0], ..]);
        let unsqueeze285_out1 = [gather99_out1 as i64];
        let constant1548_out1: [i64; 1] = [64i64];
        let constant1545_out1: [i64; 1] = [1i64];
        let constant1546_out1: [i64; 1] = [1i64];
        let concat125_out1: [i64; 4usize] = [
            &constant1545_out1[..],
            &constant1546_out1[..],
            &unsqueeze285_out1[..],
            &constant1548_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze286_out1 = [gather99_out1 as i64];
        let constant1552_out1: [i64; 1] = [64i64];
        let constant1549_out1: [i64; 1] = [1i64];
        let constant1550_out1: [i64; 1] = [1i64];
        let concat126_out1: [i64; 4usize] = [
            &constant1549_out1[..],
            &constant1550_out1[..],
            &unsqueeze286_out1[..],
            &constant1552_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape96_out1 = slice131_out1.reshape(concat125_out1);
        let unsqueeze287_out1 = [gather99_out1 as i64];
        let slice132_out1 = constant474_out1
            .clone()
            .slice(s![0..unsqueeze287_out1[0], ..]);
        let reshape97_out1 = slice132_out1.reshape(concat126_out1);
        let unsqueeze288_out1 = [sub17_out1 as i64];
        let unsqueeze289_out1 = [gather99_out1 as i64];
        let slice133_out1 = constant461_out1
            .slice(s![unsqueeze288_out1[0]..unsqueeze289_out1[0], ..]);
        let unsqueeze290_out1 = [gather98_out1 as i64];
        let constant1564_out1: [i64; 1] = [64i64];
        let constant1561_out1: [i64; 1] = [1i64];
        let constant1562_out1: [i64; 1] = [1i64];
        let concat127_out1: [i64; 4usize] = [
            &constant1561_out1[..],
            &constant1562_out1[..],
            &unsqueeze290_out1[..],
            &constant1564_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze291_out1 = [gather98_out1 as i64];
        let constant1568_out1: [i64; 1] = [64i64];
        let constant1565_out1: [i64; 1] = [1i64];
        let constant1566_out1: [i64; 1] = [1i64];
        let concat128_out1: [i64; 4usize] = [
            &constant1565_out1[..],
            &constant1566_out1[..],
            &unsqueeze291_out1[..],
            &constant1568_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape98_out1 = slice133_out1.reshape(concat127_out1);
        let unsqueeze292_out1 = [sub17_out1 as i64];
        let unsqueeze293_out1 = [gather99_out1 as i64];
        let slice134_out1 = constant474_out1
            .slice(s![unsqueeze292_out1[0]..unsqueeze293_out1[0], ..]);
        let reshape99_out1 = slice134_out1.reshape(concat128_out1);
        let cast154_out1 = reshape98_out1;
        let cast155_out1 = reshape99_out1;
        let mul122_out1 = squeeze43_out1.clone().mul(cast154_out1);
        let shape122_out1: [i64; 4] = {
            let axes = &squeeze43_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather100_out1 = shape122_out1[3] as i64;
        let constant1574_out1 = 2i64;
        let div59_out1 = gather100_out1 / constant1574_out1;
        let cast156_out1 = div59_out1;
        let cast157_out1 = cast156_out1;
        let unsqueeze294_out1 = [cast157_out1 as i64];
        let slice135_out1 = squeeze43_out1
            .clone()
            .slice(s![.., .., .., unsqueeze294_out1[0]..9223372036854775807]);
        let neg29_out1 = slice135_out1.neg();
        let unsqueeze295_out1 = [cast157_out1 as i64];
        let slice136_out1 = squeeze43_out1
            .slice(s![.., .., .., 0..unsqueeze295_out1[0]]);
        let concat129_out1 = burn::tensor::Tensor::cat(
            [neg29_out1, slice136_out1].into(),
            3,
        );
        let mul123_out1 = concat129_out1.mul(cast155_out1);
        let add98_out1 = mul122_out1.add(mul123_out1);
        let cast158_out1 = reshape96_out1;
        let cast159_out1 = reshape97_out1;
        let mul124_out1 = squeeze44_out1.clone().mul(cast158_out1);
        let shape123_out1: [i64; 4] = {
            let axes = &squeeze44_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather101_out1 = shape123_out1[3] as i64;
        let constant1584_out1 = 2i64;
        let div60_out1 = gather101_out1 / constant1584_out1;
        let cast160_out1 = div60_out1;
        let cast161_out1 = cast160_out1;
        let unsqueeze296_out1 = [cast161_out1 as i64];
        let slice137_out1 = squeeze44_out1
            .clone()
            .slice(s![.., .., .., unsqueeze296_out1[0]..9223372036854775807]);
        let neg30_out1 = slice137_out1.neg();
        let unsqueeze297_out1 = [cast161_out1 as i64];
        let slice138_out1 = squeeze44_out1
            .slice(s![.., .., .., 0..unsqueeze297_out1[0]]);
        let concat130_out1 = burn::tensor::Tensor::cat(
            [neg30_out1, slice138_out1].into(),
            3,
        );
        let mul125_out1 = concat130_out1.mul(cast159_out1);
        let add99_out1 = mul124_out1.add(mul125_out1);
        let shape124_out1: [i64; 4] = {
            let axes = &add98_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let slice139_out1: [i64; 1] = shape124_out1[3..4].try_into().unwrap();
        let cast162_out1 = {
            let shape_array = slice139_out1 as [i64; 1usize];
            let float_array: [f64; 1usize] = shape_array.map(|x| x as f64);
            Tensor::<
                1,
            >::from_data(
                TensorData::from(float_array),
                (&self.device, burn::tensor::DType::F32),
            )
        };
        let sqrt43_out1 = cast162_out1.sqrt();
        let constant1595_out1 = self.constant1595.val();
        let div61_out1 = constant1595_out1.div(sqrt43_out1);
        let cast163_out1 = div61_out1;
        let transpose44_out1 = add99_out1.permute([0, 1, 3, 2]);
        let sqrt44_out1 = cast163_out1.clone().sqrt();
        let mul126_out1 = add98_out1
            .mul((sqrt44_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let sqrt45_out1 = cast163_out1.sqrt();
        let mul127_out1 = transpose44_out1
            .mul((sqrt45_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let matmul87_out1 = mul126_out1.matmul(mul127_out1);
        let constant1597_out1 = self.constant1597.val();
        let constant1596_out1 = self.constant1596.val();
        let where35_out1 = constant1597_out1
            .unsqueeze_dims(&[0isize, 1isize, 2isize])
            .mask_where(
                less4_out1,
                constant1596_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let add100_out1 = matmul87_out1.add(where35_out1);
        let softmax15_out1 = burn::tensor::activation::softmax(add100_out1, 3);
        let isnan15_out1 = softmax15_out1.clone().is_nan();
        let constant1598_out1 = self.constant1598.val();
        let where36_out1 = softmax15_out1
            .mask_where(
                isnan15_out1,
                constant1598_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let matmul88_out1 = where36_out1.matmul(squeeze45_out1);
        let transpose45_out1 = matmul88_out1.permute([0, 2, 1, 3]);
        let unsqueeze298_out1 = [gather96_out1 as i64];
        let unsqueeze299_out1 = [gather97_out1 as i64];
        let constant1601_out1: [i64; 1] = [512i64];
        let concat131_out1: [i64; 3usize] = [
            &unsqueeze298_out1[..],
            &unsqueeze299_out1[..],
            &constant1601_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape100_out1 = transpose45_out1.reshape(concat131_out1);
        let linear59_out1 = self.linear59.forward(reshape100_out1);
        let add101_out1 = add97_out1.add(linear59_out1);
        add101_out1
    }
}
#[derive(Module, Debug)]
pub struct Submodule17 {
    layernormalization31: LayerNorm,
    linear60: Linear,
    constant1602: burn::module::Param<Tensor<1>>,
    constant1603: burn::module::Param<Tensor<1>>,
    constant1604: burn::module::Param<Tensor<1>>,
    linear61: Linear,
    layernormalization32: LayerNorm,
    linear62: Linear,
    constant1672: burn::module::Param<Tensor<1>>,
    constant1674: burn::module::Param<Tensor<1>>,
    constant1673: burn::module::Param<Tensor<1>>,
    constant1675: burn::module::Param<Tensor<1>>,
    linear63: Linear,
    #[module(skip)]
    device: Device,
}
impl Submodule17 {
    #[allow(unused_variables)]
    pub fn new(device: &Device) -> Self {
        let layernormalization31 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear60 = LinearConfig::new(512, 2048).with_bias(true).init(device);
        let constant1602: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1.4142135381698608f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1603: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1604: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([0.5f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let linear61 = LinearConfig::new(2048, 512).with_bias(true).init(device);
        let layernormalization32 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear62 = LinearConfig::new(512, 1536).with_bias(false).init(device);
        let constant1672: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1674: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1673: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1675: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let linear63 = LinearConfig::new(512, 512).with_bias(true).init(device);
        Self {
            layernormalization31,
            linear60,
            constant1602,
            constant1603,
            constant1604,
            linear61,
            layernormalization32,
            linear62,
            constant1672,
            constant1674,
            constant1673,
            constant1675,
            linear63,
            device: device.clone(),
        }
    }
    #[allow(clippy::let_and_return, clippy::approx_constant)]
    pub fn forward(
        &self,
        add101_out1: Tensor<3>,
        constant461_out1: Tensor<2>,
        constant474_out1: Tensor<2>,
        less4_out1: Tensor<4, Bool>,
    ) -> Tensor<3> {
        let layernormalization31_out1 = {
            let dtype = add101_out1.clone().dtype();
            self.layernormalization31
                .forward(add101_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let linear60_out1 = self.linear60.forward(layernormalization31_out1);
        let constant1602_out1 = self.constant1602.val();
        let div62_out1 = linear60_out1
            .clone()
            .div((constant1602_out1).unsqueeze_dims(&[0isize, 1isize]));
        let erf15_out1 = div62_out1.erf();
        let constant1603_out1 = self.constant1603.val();
        let add102_out1 = erf15_out1
            .add((constant1603_out1).unsqueeze_dims(&[0isize, 1isize]));
        let mul128_out1 = linear60_out1.mul(add102_out1);
        let constant1604_out1 = self.constant1604.val();
        let mul129_out1 = mul128_out1
            .mul((constant1604_out1).unsqueeze_dims(&[0isize, 1isize]));
        let linear61_out1 = self.linear61.forward(mul129_out1);
        let add103_out1 = add101_out1.add(linear61_out1);
        let layernormalization32_out1 = {
            let dtype = add103_out1.clone().dtype();
            self.layernormalization32
                .forward(add103_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let shape125_out1: [i64; 3] = {
            let axes = &layernormalization32_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather102_out1 = shape125_out1[0] as i64;
        let shape126_out1: [i64; 3] = {
            let axes = &layernormalization32_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather103_out1 = shape126_out1[1] as i64;
        let linear62_out1 = self.linear62.forward(layernormalization32_out1);
        let unsqueeze300_out1 = [gather102_out1 as i64];
        let unsqueeze301_out1 = [gather103_out1 as i64];
        let constant1611_out1: [i64; 1] = [64i64];
        let constant1609_out1: [i64; 1] = [3i64];
        let constant1610_out1: [i64; 1] = [8i64];
        let concat132_out1: [i64; 5usize] = [
            &unsqueeze300_out1[..],
            &unsqueeze301_out1[..],
            &constant1609_out1[..],
            &constant1610_out1[..],
            &constant1611_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape101_out1 = linear62_out1.reshape(concat132_out1);
        let transpose46_out1 = reshape101_out1.permute([2, 0, 3, 1, 4]);
        let split_tensors = transpose46_out1.split_with_sizes([1, 1, 1].into(), 0);
        let [split16_out1, split16_out2, split16_out3] = split_tensors
            .try_into()
            .unwrap();
        let squeeze46_out1 = split16_out1.squeeze_dims::<4>(&[0]);
        let squeeze47_out1 = split16_out2.squeeze_dims::<4>(&[0]);
        let squeeze48_out1 = split16_out3.squeeze_dims::<4>(&[0]);
        let shape127_out1: [i64; 4] = {
            let axes = &squeeze46_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather104_out1 = shape127_out1[2] as i64;
        let shape128_out1: [i64; 4] = {
            let axes = &squeeze47_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather105_out1 = shape128_out1[2] as i64;
        let sub18_out1 = gather105_out1 - gather104_out1;
        let unsqueeze302_out1 = [gather105_out1 as i64];
        let slice140_out1 = constant461_out1
            .clone()
            .slice(s![0..unsqueeze302_out1[0], ..]);
        let unsqueeze303_out1 = [gather105_out1 as i64];
        let constant1625_out1: [i64; 1] = [64i64];
        let constant1622_out1: [i64; 1] = [1i64];
        let constant1623_out1: [i64; 1] = [1i64];
        let concat133_out1: [i64; 4usize] = [
            &constant1622_out1[..],
            &constant1623_out1[..],
            &unsqueeze303_out1[..],
            &constant1625_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze304_out1 = [gather105_out1 as i64];
        let constant1629_out1: [i64; 1] = [64i64];
        let constant1626_out1: [i64; 1] = [1i64];
        let constant1627_out1: [i64; 1] = [1i64];
        let concat134_out1: [i64; 4usize] = [
            &constant1626_out1[..],
            &constant1627_out1[..],
            &unsqueeze304_out1[..],
            &constant1629_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape102_out1 = slice140_out1.reshape(concat133_out1);
        let unsqueeze305_out1 = [gather105_out1 as i64];
        let slice141_out1 = constant474_out1
            .clone()
            .slice(s![0..unsqueeze305_out1[0], ..]);
        let reshape103_out1 = slice141_out1.reshape(concat134_out1);
        let unsqueeze306_out1 = [sub18_out1 as i64];
        let unsqueeze307_out1 = [gather105_out1 as i64];
        let slice142_out1 = constant461_out1
            .slice(s![unsqueeze306_out1[0]..unsqueeze307_out1[0], ..]);
        let unsqueeze308_out1 = [gather104_out1 as i64];
        let constant1641_out1: [i64; 1] = [64i64];
        let constant1638_out1: [i64; 1] = [1i64];
        let constant1639_out1: [i64; 1] = [1i64];
        let concat135_out1: [i64; 4usize] = [
            &constant1638_out1[..],
            &constant1639_out1[..],
            &unsqueeze308_out1[..],
            &constant1641_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze309_out1 = [gather104_out1 as i64];
        let constant1645_out1: [i64; 1] = [64i64];
        let constant1642_out1: [i64; 1] = [1i64];
        let constant1643_out1: [i64; 1] = [1i64];
        let concat136_out1: [i64; 4usize] = [
            &constant1642_out1[..],
            &constant1643_out1[..],
            &unsqueeze309_out1[..],
            &constant1645_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape104_out1 = slice142_out1.reshape(concat135_out1);
        let unsqueeze310_out1 = [sub18_out1 as i64];
        let unsqueeze311_out1 = [gather105_out1 as i64];
        let slice143_out1 = constant474_out1
            .slice(s![unsqueeze310_out1[0]..unsqueeze311_out1[0], ..]);
        let reshape105_out1 = slice143_out1.reshape(concat136_out1);
        let cast164_out1 = reshape104_out1;
        let cast165_out1 = reshape105_out1;
        let mul130_out1 = squeeze46_out1.clone().mul(cast164_out1);
        let shape129_out1: [i64; 4] = {
            let axes = &squeeze46_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather106_out1 = shape129_out1[3] as i64;
        let constant1651_out1 = 2i64;
        let div63_out1 = gather106_out1 / constant1651_out1;
        let cast166_out1 = div63_out1;
        let cast167_out1 = cast166_out1;
        let unsqueeze312_out1 = [cast167_out1 as i64];
        let slice144_out1 = squeeze46_out1
            .clone()
            .slice(s![.., .., .., unsqueeze312_out1[0]..9223372036854775807]);
        let neg31_out1 = slice144_out1.neg();
        let unsqueeze313_out1 = [cast167_out1 as i64];
        let slice145_out1 = squeeze46_out1
            .slice(s![.., .., .., 0..unsqueeze313_out1[0]]);
        let concat137_out1 = burn::tensor::Tensor::cat(
            [neg31_out1, slice145_out1].into(),
            3,
        );
        let mul131_out1 = concat137_out1.mul(cast165_out1);
        let add104_out1 = mul130_out1.add(mul131_out1);
        let cast168_out1 = reshape102_out1;
        let cast169_out1 = reshape103_out1;
        let mul132_out1 = squeeze47_out1.clone().mul(cast168_out1);
        let shape130_out1: [i64; 4] = {
            let axes = &squeeze47_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather107_out1 = shape130_out1[3] as i64;
        let constant1661_out1 = 2i64;
        let div64_out1 = gather107_out1 / constant1661_out1;
        let cast170_out1 = div64_out1;
        let cast171_out1 = cast170_out1;
        let unsqueeze314_out1 = [cast171_out1 as i64];
        let slice146_out1 = squeeze47_out1
            .clone()
            .slice(s![.., .., .., unsqueeze314_out1[0]..9223372036854775807]);
        let neg32_out1 = slice146_out1.neg();
        let unsqueeze315_out1 = [cast171_out1 as i64];
        let slice147_out1 = squeeze47_out1
            .slice(s![.., .., .., 0..unsqueeze315_out1[0]]);
        let concat138_out1 = burn::tensor::Tensor::cat(
            [neg32_out1, slice147_out1].into(),
            3,
        );
        let mul133_out1 = concat138_out1.mul(cast169_out1);
        let add105_out1 = mul132_out1.add(mul133_out1);
        let shape131_out1: [i64; 4] = {
            let axes = &add104_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let slice148_out1: [i64; 1] = shape131_out1[3..4].try_into().unwrap();
        let cast172_out1 = {
            let shape_array = slice148_out1 as [i64; 1usize];
            let float_array: [f64; 1usize] = shape_array.map(|x| x as f64);
            Tensor::<
                1,
            >::from_data(
                TensorData::from(float_array),
                (&self.device, burn::tensor::DType::F32),
            )
        };
        let sqrt46_out1 = cast172_out1.sqrt();
        let constant1672_out1 = self.constant1672.val();
        let div65_out1 = constant1672_out1.div(sqrt46_out1);
        let cast173_out1 = div65_out1;
        let transpose47_out1 = add105_out1.permute([0, 1, 3, 2]);
        let sqrt47_out1 = cast173_out1.clone().sqrt();
        let mul134_out1 = add104_out1
            .mul((sqrt47_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let sqrt48_out1 = cast173_out1.sqrt();
        let mul135_out1 = transpose47_out1
            .mul((sqrt48_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let matmul93_out1 = mul134_out1.matmul(mul135_out1);
        let constant1674_out1 = self.constant1674.val();
        let constant1673_out1 = self.constant1673.val();
        let where37_out1 = constant1674_out1
            .unsqueeze_dims(&[0isize, 1isize, 2isize])
            .mask_where(
                less4_out1,
                constant1673_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let add106_out1 = matmul93_out1.add(where37_out1);
        let softmax16_out1 = burn::tensor::activation::softmax(add106_out1, 3);
        let isnan16_out1 = softmax16_out1.clone().is_nan();
        let constant1675_out1 = self.constant1675.val();
        let where38_out1 = softmax16_out1
            .mask_where(
                isnan16_out1,
                constant1675_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let matmul94_out1 = where38_out1.matmul(squeeze48_out1);
        let transpose48_out1 = matmul94_out1.permute([0, 2, 1, 3]);
        let unsqueeze316_out1 = [gather102_out1 as i64];
        let unsqueeze317_out1 = [gather103_out1 as i64];
        let constant1678_out1: [i64; 1] = [512i64];
        let concat139_out1: [i64; 3usize] = [
            &unsqueeze316_out1[..],
            &unsqueeze317_out1[..],
            &constant1678_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape106_out1 = transpose48_out1.reshape(concat139_out1);
        let linear63_out1 = self.linear63.forward(reshape106_out1);
        let add107_out1 = add103_out1.add(linear63_out1);
        add107_out1
    }
}
#[derive(Module, Debug)]
pub struct Submodule18 {
    layernormalization33: LayerNorm,
    linear64: Linear,
    constant1679: burn::module::Param<Tensor<1>>,
    constant1680: burn::module::Param<Tensor<1>>,
    constant1681: burn::module::Param<Tensor<1>>,
    linear65: Linear,
    layernormalization34: LayerNorm,
    linear66: Linear,
    constant1749: burn::module::Param<Tensor<1>>,
    constant1751: burn::module::Param<Tensor<1>>,
    constant1750: burn::module::Param<Tensor<1>>,
    constant1752: burn::module::Param<Tensor<1>>,
    linear67: Linear,
    #[module(skip)]
    device: Device,
}
impl Submodule18 {
    #[allow(unused_variables)]
    pub fn new(device: &Device) -> Self {
        let layernormalization33 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear64 = LinearConfig::new(512, 2048).with_bias(true).init(device);
        let constant1679: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1.4142135381698608f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1680: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1681: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([0.5f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let linear65 = LinearConfig::new(2048, 512).with_bias(true).init(device);
        let layernormalization34 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear66 = LinearConfig::new(512, 1536).with_bias(false).init(device);
        let constant1749: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1751: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1750: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1752: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let linear67 = LinearConfig::new(512, 512).with_bias(true).init(device);
        Self {
            layernormalization33,
            linear64,
            constant1679,
            constant1680,
            constant1681,
            linear65,
            layernormalization34,
            linear66,
            constant1749,
            constant1751,
            constant1750,
            constant1752,
            linear67,
            device: device.clone(),
        }
    }
    #[allow(clippy::let_and_return, clippy::approx_constant)]
    pub fn forward(
        &self,
        add107_out1: Tensor<3>,
        constant461_out1: Tensor<2>,
        constant474_out1: Tensor<2>,
        less4_out1: Tensor<4, Bool>,
    ) -> Tensor<3> {
        let layernormalization33_out1 = {
            let dtype = add107_out1.clone().dtype();
            self.layernormalization33
                .forward(add107_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let linear64_out1 = self.linear64.forward(layernormalization33_out1);
        let constant1679_out1 = self.constant1679.val();
        let div66_out1 = linear64_out1
            .clone()
            .div((constant1679_out1).unsqueeze_dims(&[0isize, 1isize]));
        let erf16_out1 = div66_out1.erf();
        let constant1680_out1 = self.constant1680.val();
        let add108_out1 = erf16_out1
            .add((constant1680_out1).unsqueeze_dims(&[0isize, 1isize]));
        let mul136_out1 = linear64_out1.mul(add108_out1);
        let constant1681_out1 = self.constant1681.val();
        let mul137_out1 = mul136_out1
            .mul((constant1681_out1).unsqueeze_dims(&[0isize, 1isize]));
        let linear65_out1 = self.linear65.forward(mul137_out1);
        let add109_out1 = add107_out1.add(linear65_out1);
        let layernormalization34_out1 = {
            let dtype = add109_out1.clone().dtype();
            self.layernormalization34
                .forward(add109_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let shape132_out1: [i64; 3] = {
            let axes = &layernormalization34_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather108_out1 = shape132_out1[0] as i64;
        let shape133_out1: [i64; 3] = {
            let axes = &layernormalization34_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather109_out1 = shape133_out1[1] as i64;
        let linear66_out1 = self.linear66.forward(layernormalization34_out1);
        let unsqueeze318_out1 = [gather108_out1 as i64];
        let unsqueeze319_out1 = [gather109_out1 as i64];
        let constant1688_out1: [i64; 1] = [64i64];
        let constant1686_out1: [i64; 1] = [3i64];
        let constant1687_out1: [i64; 1] = [8i64];
        let concat140_out1: [i64; 5usize] = [
            &unsqueeze318_out1[..],
            &unsqueeze319_out1[..],
            &constant1686_out1[..],
            &constant1687_out1[..],
            &constant1688_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape107_out1 = linear66_out1.reshape(concat140_out1);
        let transpose49_out1 = reshape107_out1.permute([2, 0, 3, 1, 4]);
        let split_tensors = transpose49_out1.split_with_sizes([1, 1, 1].into(), 0);
        let [split17_out1, split17_out2, split17_out3] = split_tensors
            .try_into()
            .unwrap();
        let squeeze49_out1 = split17_out1.squeeze_dims::<4>(&[0]);
        let squeeze50_out1 = split17_out2.squeeze_dims::<4>(&[0]);
        let squeeze51_out1 = split17_out3.squeeze_dims::<4>(&[0]);
        let shape134_out1: [i64; 4] = {
            let axes = &squeeze49_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather110_out1 = shape134_out1[2] as i64;
        let shape135_out1: [i64; 4] = {
            let axes = &squeeze50_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather111_out1 = shape135_out1[2] as i64;
        let sub19_out1 = gather111_out1 - gather110_out1;
        let unsqueeze320_out1 = [gather111_out1 as i64];
        let slice149_out1 = constant461_out1
            .clone()
            .slice(s![0..unsqueeze320_out1[0], ..]);
        let unsqueeze321_out1 = [gather111_out1 as i64];
        let constant1702_out1: [i64; 1] = [64i64];
        let constant1699_out1: [i64; 1] = [1i64];
        let constant1700_out1: [i64; 1] = [1i64];
        let concat141_out1: [i64; 4usize] = [
            &constant1699_out1[..],
            &constant1700_out1[..],
            &unsqueeze321_out1[..],
            &constant1702_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze322_out1 = [gather111_out1 as i64];
        let constant1706_out1: [i64; 1] = [64i64];
        let constant1703_out1: [i64; 1] = [1i64];
        let constant1704_out1: [i64; 1] = [1i64];
        let concat142_out1: [i64; 4usize] = [
            &constant1703_out1[..],
            &constant1704_out1[..],
            &unsqueeze322_out1[..],
            &constant1706_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape108_out1 = slice149_out1.reshape(concat141_out1);
        let unsqueeze323_out1 = [gather111_out1 as i64];
        let slice150_out1 = constant474_out1
            .clone()
            .slice(s![0..unsqueeze323_out1[0], ..]);
        let reshape109_out1 = slice150_out1.reshape(concat142_out1);
        let unsqueeze324_out1 = [sub19_out1 as i64];
        let unsqueeze325_out1 = [gather111_out1 as i64];
        let slice151_out1 = constant461_out1
            .slice(s![unsqueeze324_out1[0]..unsqueeze325_out1[0], ..]);
        let unsqueeze326_out1 = [gather110_out1 as i64];
        let constant1718_out1: [i64; 1] = [64i64];
        let constant1715_out1: [i64; 1] = [1i64];
        let constant1716_out1: [i64; 1] = [1i64];
        let concat143_out1: [i64; 4usize] = [
            &constant1715_out1[..],
            &constant1716_out1[..],
            &unsqueeze326_out1[..],
            &constant1718_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze327_out1 = [gather110_out1 as i64];
        let constant1722_out1: [i64; 1] = [64i64];
        let constant1719_out1: [i64; 1] = [1i64];
        let constant1720_out1: [i64; 1] = [1i64];
        let concat144_out1: [i64; 4usize] = [
            &constant1719_out1[..],
            &constant1720_out1[..],
            &unsqueeze327_out1[..],
            &constant1722_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape110_out1 = slice151_out1.reshape(concat143_out1);
        let unsqueeze328_out1 = [sub19_out1 as i64];
        let unsqueeze329_out1 = [gather111_out1 as i64];
        let slice152_out1 = constant474_out1
            .slice(s![unsqueeze328_out1[0]..unsqueeze329_out1[0], ..]);
        let reshape111_out1 = slice152_out1.reshape(concat144_out1);
        let cast174_out1 = reshape110_out1;
        let cast175_out1 = reshape111_out1;
        let mul138_out1 = squeeze49_out1.clone().mul(cast174_out1);
        let shape136_out1: [i64; 4] = {
            let axes = &squeeze49_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather112_out1 = shape136_out1[3] as i64;
        let constant1728_out1 = 2i64;
        let div67_out1 = gather112_out1 / constant1728_out1;
        let cast176_out1 = div67_out1;
        let cast177_out1 = cast176_out1;
        let unsqueeze330_out1 = [cast177_out1 as i64];
        let slice153_out1 = squeeze49_out1
            .clone()
            .slice(s![.., .., .., unsqueeze330_out1[0]..9223372036854775807]);
        let neg33_out1 = slice153_out1.neg();
        let unsqueeze331_out1 = [cast177_out1 as i64];
        let slice154_out1 = squeeze49_out1
            .slice(s![.., .., .., 0..unsqueeze331_out1[0]]);
        let concat145_out1 = burn::tensor::Tensor::cat(
            [neg33_out1, slice154_out1].into(),
            3,
        );
        let mul139_out1 = concat145_out1.mul(cast175_out1);
        let add110_out1 = mul138_out1.add(mul139_out1);
        let cast178_out1 = reshape108_out1;
        let cast179_out1 = reshape109_out1;
        let mul140_out1 = squeeze50_out1.clone().mul(cast178_out1);
        let shape137_out1: [i64; 4] = {
            let axes = &squeeze50_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather113_out1 = shape137_out1[3] as i64;
        let constant1738_out1 = 2i64;
        let div68_out1 = gather113_out1 / constant1738_out1;
        let cast180_out1 = div68_out1;
        let cast181_out1 = cast180_out1;
        let unsqueeze332_out1 = [cast181_out1 as i64];
        let slice155_out1 = squeeze50_out1
            .clone()
            .slice(s![.., .., .., unsqueeze332_out1[0]..9223372036854775807]);
        let neg34_out1 = slice155_out1.neg();
        let unsqueeze333_out1 = [cast181_out1 as i64];
        let slice156_out1 = squeeze50_out1
            .slice(s![.., .., .., 0..unsqueeze333_out1[0]]);
        let concat146_out1 = burn::tensor::Tensor::cat(
            [neg34_out1, slice156_out1].into(),
            3,
        );
        let mul141_out1 = concat146_out1.mul(cast179_out1);
        let add111_out1 = mul140_out1.add(mul141_out1);
        let shape138_out1: [i64; 4] = {
            let axes = &add110_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let slice157_out1: [i64; 1] = shape138_out1[3..4].try_into().unwrap();
        let cast182_out1 = {
            let shape_array = slice157_out1 as [i64; 1usize];
            let float_array: [f64; 1usize] = shape_array.map(|x| x as f64);
            Tensor::<
                1,
            >::from_data(
                TensorData::from(float_array),
                (&self.device, burn::tensor::DType::F32),
            )
        };
        let sqrt49_out1 = cast182_out1.sqrt();
        let constant1749_out1 = self.constant1749.val();
        let div69_out1 = constant1749_out1.div(sqrt49_out1);
        let cast183_out1 = div69_out1;
        let transpose50_out1 = add111_out1.permute([0, 1, 3, 2]);
        let sqrt50_out1 = cast183_out1.clone().sqrt();
        let mul142_out1 = add110_out1
            .mul((sqrt50_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let sqrt51_out1 = cast183_out1.sqrt();
        let mul143_out1 = transpose50_out1
            .mul((sqrt51_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let matmul99_out1 = mul142_out1.matmul(mul143_out1);
        let constant1751_out1 = self.constant1751.val();
        let constant1750_out1 = self.constant1750.val();
        let where39_out1 = constant1751_out1
            .unsqueeze_dims(&[0isize, 1isize, 2isize])
            .mask_where(
                less4_out1,
                constant1750_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let add112_out1 = matmul99_out1.add(where39_out1);
        let softmax17_out1 = burn::tensor::activation::softmax(add112_out1, 3);
        let isnan17_out1 = softmax17_out1.clone().is_nan();
        let constant1752_out1 = self.constant1752.val();
        let where40_out1 = softmax17_out1
            .mask_where(
                isnan17_out1,
                constant1752_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let matmul100_out1 = where40_out1.matmul(squeeze51_out1);
        let transpose51_out1 = matmul100_out1.permute([0, 2, 1, 3]);
        let unsqueeze334_out1 = [gather108_out1 as i64];
        let unsqueeze335_out1 = [gather109_out1 as i64];
        let constant1755_out1: [i64; 1] = [512i64];
        let concat147_out1: [i64; 3usize] = [
            &unsqueeze334_out1[..],
            &unsqueeze335_out1[..],
            &constant1755_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape112_out1 = transpose51_out1.reshape(concat147_out1);
        let linear67_out1 = self.linear67.forward(reshape112_out1);
        let add113_out1 = add109_out1.add(linear67_out1);
        add113_out1
    }
}
#[derive(Module, Debug)]
pub struct Submodule19 {
    layernormalization35: LayerNorm,
    linear68: Linear,
    constant1756: burn::module::Param<Tensor<1>>,
    constant1757: burn::module::Param<Tensor<1>>,
    constant1758: burn::module::Param<Tensor<1>>,
    linear69: Linear,
    layernormalization36: LayerNorm,
    linear70: Linear,
    constant1826: burn::module::Param<Tensor<1>>,
    constant1828: burn::module::Param<Tensor<1>>,
    constant1827: burn::module::Param<Tensor<1>>,
    constant1829: burn::module::Param<Tensor<1>>,
    linear71: Linear,
    #[module(skip)]
    device: Device,
}
impl Submodule19 {
    #[allow(unused_variables)]
    pub fn new(device: &Device) -> Self {
        let layernormalization35 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear68 = LinearConfig::new(512, 2048).with_bias(true).init(device);
        let constant1756: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1.4142135381698608f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1757: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1758: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([0.5f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let linear69 = LinearConfig::new(2048, 512).with_bias(true).init(device);
        let layernormalization36 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear70 = LinearConfig::new(512, 1536).with_bias(false).init(device);
        let constant1826: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1828: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1827: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1829: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let linear71 = LinearConfig::new(512, 512).with_bias(true).init(device);
        Self {
            layernormalization35,
            linear68,
            constant1756,
            constant1757,
            constant1758,
            linear69,
            layernormalization36,
            linear70,
            constant1826,
            constant1828,
            constant1827,
            constant1829,
            linear71,
            device: device.clone(),
        }
    }
    #[allow(clippy::let_and_return, clippy::approx_constant)]
    pub fn forward(
        &self,
        add113_out1: Tensor<3>,
        constant461_out1: Tensor<2>,
        constant474_out1: Tensor<2>,
        less4_out1: Tensor<4, Bool>,
    ) -> Tensor<3> {
        let layernormalization35_out1 = {
            let dtype = add113_out1.clone().dtype();
            self.layernormalization35
                .forward(add113_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let linear68_out1 = self.linear68.forward(layernormalization35_out1);
        let constant1756_out1 = self.constant1756.val();
        let div70_out1 = linear68_out1
            .clone()
            .div((constant1756_out1).unsqueeze_dims(&[0isize, 1isize]));
        let erf17_out1 = div70_out1.erf();
        let constant1757_out1 = self.constant1757.val();
        let add114_out1 = erf17_out1
            .add((constant1757_out1).unsqueeze_dims(&[0isize, 1isize]));
        let mul144_out1 = linear68_out1.mul(add114_out1);
        let constant1758_out1 = self.constant1758.val();
        let mul145_out1 = mul144_out1
            .mul((constant1758_out1).unsqueeze_dims(&[0isize, 1isize]));
        let linear69_out1 = self.linear69.forward(mul145_out1);
        let add115_out1 = add113_out1.add(linear69_out1);
        let layernormalization36_out1 = {
            let dtype = add115_out1.clone().dtype();
            self.layernormalization36
                .forward(add115_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let shape139_out1: [i64; 3] = {
            let axes = &layernormalization36_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather114_out1 = shape139_out1[0] as i64;
        let shape140_out1: [i64; 3] = {
            let axes = &layernormalization36_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather115_out1 = shape140_out1[1] as i64;
        let linear70_out1 = self.linear70.forward(layernormalization36_out1);
        let unsqueeze336_out1 = [gather114_out1 as i64];
        let unsqueeze337_out1 = [gather115_out1 as i64];
        let constant1765_out1: [i64; 1] = [64i64];
        let constant1763_out1: [i64; 1] = [3i64];
        let constant1764_out1: [i64; 1] = [8i64];
        let concat148_out1: [i64; 5usize] = [
            &unsqueeze336_out1[..],
            &unsqueeze337_out1[..],
            &constant1763_out1[..],
            &constant1764_out1[..],
            &constant1765_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape113_out1 = linear70_out1.reshape(concat148_out1);
        let transpose52_out1 = reshape113_out1.permute([2, 0, 3, 1, 4]);
        let split_tensors = transpose52_out1.split_with_sizes([1, 1, 1].into(), 0);
        let [split18_out1, split18_out2, split18_out3] = split_tensors
            .try_into()
            .unwrap();
        let squeeze52_out1 = split18_out1.squeeze_dims::<4>(&[0]);
        let squeeze53_out1 = split18_out2.squeeze_dims::<4>(&[0]);
        let squeeze54_out1 = split18_out3.squeeze_dims::<4>(&[0]);
        let shape141_out1: [i64; 4] = {
            let axes = &squeeze52_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather116_out1 = shape141_out1[2] as i64;
        let shape142_out1: [i64; 4] = {
            let axes = &squeeze53_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather117_out1 = shape142_out1[2] as i64;
        let sub20_out1 = gather117_out1 - gather116_out1;
        let unsqueeze338_out1 = [gather117_out1 as i64];
        let slice158_out1 = constant461_out1
            .clone()
            .slice(s![0..unsqueeze338_out1[0], ..]);
        let unsqueeze339_out1 = [gather117_out1 as i64];
        let constant1779_out1: [i64; 1] = [64i64];
        let constant1776_out1: [i64; 1] = [1i64];
        let constant1777_out1: [i64; 1] = [1i64];
        let concat149_out1: [i64; 4usize] = [
            &constant1776_out1[..],
            &constant1777_out1[..],
            &unsqueeze339_out1[..],
            &constant1779_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze340_out1 = [gather117_out1 as i64];
        let constant1783_out1: [i64; 1] = [64i64];
        let constant1780_out1: [i64; 1] = [1i64];
        let constant1781_out1: [i64; 1] = [1i64];
        let concat150_out1: [i64; 4usize] = [
            &constant1780_out1[..],
            &constant1781_out1[..],
            &unsqueeze340_out1[..],
            &constant1783_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape114_out1 = slice158_out1.reshape(concat149_out1);
        let unsqueeze341_out1 = [gather117_out1 as i64];
        let slice159_out1 = constant474_out1
            .clone()
            .slice(s![0..unsqueeze341_out1[0], ..]);
        let reshape115_out1 = slice159_out1.reshape(concat150_out1);
        let unsqueeze342_out1 = [sub20_out1 as i64];
        let unsqueeze343_out1 = [gather117_out1 as i64];
        let slice160_out1 = constant461_out1
            .slice(s![unsqueeze342_out1[0]..unsqueeze343_out1[0], ..]);
        let unsqueeze344_out1 = [gather116_out1 as i64];
        let constant1795_out1: [i64; 1] = [64i64];
        let constant1792_out1: [i64; 1] = [1i64];
        let constant1793_out1: [i64; 1] = [1i64];
        let concat151_out1: [i64; 4usize] = [
            &constant1792_out1[..],
            &constant1793_out1[..],
            &unsqueeze344_out1[..],
            &constant1795_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze345_out1 = [gather116_out1 as i64];
        let constant1799_out1: [i64; 1] = [64i64];
        let constant1796_out1: [i64; 1] = [1i64];
        let constant1797_out1: [i64; 1] = [1i64];
        let concat152_out1: [i64; 4usize] = [
            &constant1796_out1[..],
            &constant1797_out1[..],
            &unsqueeze345_out1[..],
            &constant1799_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape116_out1 = slice160_out1.reshape(concat151_out1);
        let unsqueeze346_out1 = [sub20_out1 as i64];
        let unsqueeze347_out1 = [gather117_out1 as i64];
        let slice161_out1 = constant474_out1
            .slice(s![unsqueeze346_out1[0]..unsqueeze347_out1[0], ..]);
        let reshape117_out1 = slice161_out1.reshape(concat152_out1);
        let cast184_out1 = reshape116_out1;
        let cast185_out1 = reshape117_out1;
        let mul146_out1 = squeeze52_out1.clone().mul(cast184_out1);
        let shape143_out1: [i64; 4] = {
            let axes = &squeeze52_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather118_out1 = shape143_out1[3] as i64;
        let constant1805_out1 = 2i64;
        let div71_out1 = gather118_out1 / constant1805_out1;
        let cast186_out1 = div71_out1;
        let cast187_out1 = cast186_out1;
        let unsqueeze348_out1 = [cast187_out1 as i64];
        let slice162_out1 = squeeze52_out1
            .clone()
            .slice(s![.., .., .., unsqueeze348_out1[0]..9223372036854775807]);
        let neg35_out1 = slice162_out1.neg();
        let unsqueeze349_out1 = [cast187_out1 as i64];
        let slice163_out1 = squeeze52_out1
            .slice(s![.., .., .., 0..unsqueeze349_out1[0]]);
        let concat153_out1 = burn::tensor::Tensor::cat(
            [neg35_out1, slice163_out1].into(),
            3,
        );
        let mul147_out1 = concat153_out1.mul(cast185_out1);
        let add116_out1 = mul146_out1.add(mul147_out1);
        let cast188_out1 = reshape114_out1;
        let cast189_out1 = reshape115_out1;
        let mul148_out1 = squeeze53_out1.clone().mul(cast188_out1);
        let shape144_out1: [i64; 4] = {
            let axes = &squeeze53_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather119_out1 = shape144_out1[3] as i64;
        let constant1815_out1 = 2i64;
        let div72_out1 = gather119_out1 / constant1815_out1;
        let cast190_out1 = div72_out1;
        let cast191_out1 = cast190_out1;
        let unsqueeze350_out1 = [cast191_out1 as i64];
        let slice164_out1 = squeeze53_out1
            .clone()
            .slice(s![.., .., .., unsqueeze350_out1[0]..9223372036854775807]);
        let neg36_out1 = slice164_out1.neg();
        let unsqueeze351_out1 = [cast191_out1 as i64];
        let slice165_out1 = squeeze53_out1
            .slice(s![.., .., .., 0..unsqueeze351_out1[0]]);
        let concat154_out1 = burn::tensor::Tensor::cat(
            [neg36_out1, slice165_out1].into(),
            3,
        );
        let mul149_out1 = concat154_out1.mul(cast189_out1);
        let add117_out1 = mul148_out1.add(mul149_out1);
        let shape145_out1: [i64; 4] = {
            let axes = &add116_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let slice166_out1: [i64; 1] = shape145_out1[3..4].try_into().unwrap();
        let cast192_out1 = {
            let shape_array = slice166_out1 as [i64; 1usize];
            let float_array: [f64; 1usize] = shape_array.map(|x| x as f64);
            Tensor::<
                1,
            >::from_data(
                TensorData::from(float_array),
                (&self.device, burn::tensor::DType::F32),
            )
        };
        let sqrt52_out1 = cast192_out1.sqrt();
        let constant1826_out1 = self.constant1826.val();
        let div73_out1 = constant1826_out1.div(sqrt52_out1);
        let cast193_out1 = div73_out1;
        let transpose53_out1 = add117_out1.permute([0, 1, 3, 2]);
        let sqrt53_out1 = cast193_out1.clone().sqrt();
        let mul150_out1 = add116_out1
            .mul((sqrt53_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let sqrt54_out1 = cast193_out1.sqrt();
        let mul151_out1 = transpose53_out1
            .mul((sqrt54_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let matmul105_out1 = mul150_out1.matmul(mul151_out1);
        let constant1828_out1 = self.constant1828.val();
        let constant1827_out1 = self.constant1827.val();
        let where41_out1 = constant1828_out1
            .unsqueeze_dims(&[0isize, 1isize, 2isize])
            .mask_where(
                less4_out1,
                constant1827_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let add118_out1 = matmul105_out1.add(where41_out1);
        let softmax18_out1 = burn::tensor::activation::softmax(add118_out1, 3);
        let isnan18_out1 = softmax18_out1.clone().is_nan();
        let constant1829_out1 = self.constant1829.val();
        let where42_out1 = softmax18_out1
            .mask_where(
                isnan18_out1,
                constant1829_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let matmul106_out1 = where42_out1.matmul(squeeze54_out1);
        let transpose54_out1 = matmul106_out1.permute([0, 2, 1, 3]);
        let unsqueeze352_out1 = [gather114_out1 as i64];
        let unsqueeze353_out1 = [gather115_out1 as i64];
        let constant1832_out1: [i64; 1] = [512i64];
        let concat155_out1: [i64; 3usize] = [
            &unsqueeze352_out1[..],
            &unsqueeze353_out1[..],
            &constant1832_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape118_out1 = transpose54_out1.reshape(concat155_out1);
        let linear71_out1 = self.linear71.forward(reshape118_out1);
        let add119_out1 = add115_out1.add(linear71_out1);
        add119_out1
    }
}
#[derive(Module, Debug)]
pub struct Submodule20 {
    layernormalization37: LayerNorm,
    linear72: Linear,
    constant1833: burn::module::Param<Tensor<1>>,
    constant1834: burn::module::Param<Tensor<1>>,
    constant1835: burn::module::Param<Tensor<1>>,
    linear73: Linear,
    layernormalization38: LayerNorm,
    linear74: Linear,
    constant1903: burn::module::Param<Tensor<1>>,
    constant1905: burn::module::Param<Tensor<1>>,
    constant1904: burn::module::Param<Tensor<1>>,
    constant1906: burn::module::Param<Tensor<1>>,
    linear75: Linear,
    #[module(skip)]
    device: Device,
}
impl Submodule20 {
    #[allow(unused_variables)]
    pub fn new(device: &Device) -> Self {
        let layernormalization37 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear72 = LinearConfig::new(512, 2048).with_bias(true).init(device);
        let constant1833: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1.4142135381698608f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1834: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1835: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([0.5f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let linear73 = LinearConfig::new(2048, 512).with_bias(true).init(device);
        let layernormalization38 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear74 = LinearConfig::new(512, 1536).with_bias(false).init(device);
        let constant1903: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1905: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1904: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1906: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let linear75 = LinearConfig::new(512, 512).with_bias(true).init(device);
        Self {
            layernormalization37,
            linear72,
            constant1833,
            constant1834,
            constant1835,
            linear73,
            layernormalization38,
            linear74,
            constant1903,
            constant1905,
            constant1904,
            constant1906,
            linear75,
            device: device.clone(),
        }
    }
    #[allow(clippy::let_and_return, clippy::approx_constant)]
    pub fn forward(
        &self,
        add119_out1: Tensor<3>,
        constant461_out1: Tensor<2>,
        constant474_out1: Tensor<2>,
        less4_out1: Tensor<4, Bool>,
    ) -> Tensor<3> {
        let layernormalization37_out1 = {
            let dtype = add119_out1.clone().dtype();
            self.layernormalization37
                .forward(add119_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let linear72_out1 = self.linear72.forward(layernormalization37_out1);
        let constant1833_out1 = self.constant1833.val();
        let div74_out1 = linear72_out1
            .clone()
            .div((constant1833_out1).unsqueeze_dims(&[0isize, 1isize]));
        let erf18_out1 = div74_out1.erf();
        let constant1834_out1 = self.constant1834.val();
        let add120_out1 = erf18_out1
            .add((constant1834_out1).unsqueeze_dims(&[0isize, 1isize]));
        let mul152_out1 = linear72_out1.mul(add120_out1);
        let constant1835_out1 = self.constant1835.val();
        let mul153_out1 = mul152_out1
            .mul((constant1835_out1).unsqueeze_dims(&[0isize, 1isize]));
        let linear73_out1 = self.linear73.forward(mul153_out1);
        let add121_out1 = add119_out1.add(linear73_out1);
        let layernormalization38_out1 = {
            let dtype = add121_out1.clone().dtype();
            self.layernormalization38
                .forward(add121_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let shape146_out1: [i64; 3] = {
            let axes = &layernormalization38_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather120_out1 = shape146_out1[0] as i64;
        let shape147_out1: [i64; 3] = {
            let axes = &layernormalization38_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather121_out1 = shape147_out1[1] as i64;
        let linear74_out1 = self.linear74.forward(layernormalization38_out1);
        let unsqueeze354_out1 = [gather120_out1 as i64];
        let unsqueeze355_out1 = [gather121_out1 as i64];
        let constant1842_out1: [i64; 1] = [64i64];
        let constant1840_out1: [i64; 1] = [3i64];
        let constant1841_out1: [i64; 1] = [8i64];
        let concat156_out1: [i64; 5usize] = [
            &unsqueeze354_out1[..],
            &unsqueeze355_out1[..],
            &constant1840_out1[..],
            &constant1841_out1[..],
            &constant1842_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape119_out1 = linear74_out1.reshape(concat156_out1);
        let transpose55_out1 = reshape119_out1.permute([2, 0, 3, 1, 4]);
        let split_tensors = transpose55_out1.split_with_sizes([1, 1, 1].into(), 0);
        let [split19_out1, split19_out2, split19_out3] = split_tensors
            .try_into()
            .unwrap();
        let squeeze55_out1 = split19_out1.squeeze_dims::<4>(&[0]);
        let squeeze56_out1 = split19_out2.squeeze_dims::<4>(&[0]);
        let squeeze57_out1 = split19_out3.squeeze_dims::<4>(&[0]);
        let shape148_out1: [i64; 4] = {
            let axes = &squeeze55_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather122_out1 = shape148_out1[2] as i64;
        let shape149_out1: [i64; 4] = {
            let axes = &squeeze56_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather123_out1 = shape149_out1[2] as i64;
        let sub21_out1 = gather123_out1 - gather122_out1;
        let unsqueeze356_out1 = [gather123_out1 as i64];
        let slice167_out1 = constant461_out1
            .clone()
            .slice(s![0..unsqueeze356_out1[0], ..]);
        let unsqueeze357_out1 = [gather123_out1 as i64];
        let constant1856_out1: [i64; 1] = [64i64];
        let constant1853_out1: [i64; 1] = [1i64];
        let constant1854_out1: [i64; 1] = [1i64];
        let concat157_out1: [i64; 4usize] = [
            &constant1853_out1[..],
            &constant1854_out1[..],
            &unsqueeze357_out1[..],
            &constant1856_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze358_out1 = [gather123_out1 as i64];
        let constant1860_out1: [i64; 1] = [64i64];
        let constant1857_out1: [i64; 1] = [1i64];
        let constant1858_out1: [i64; 1] = [1i64];
        let concat158_out1: [i64; 4usize] = [
            &constant1857_out1[..],
            &constant1858_out1[..],
            &unsqueeze358_out1[..],
            &constant1860_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape120_out1 = slice167_out1.reshape(concat157_out1);
        let unsqueeze359_out1 = [gather123_out1 as i64];
        let slice168_out1 = constant474_out1
            .clone()
            .slice(s![0..unsqueeze359_out1[0], ..]);
        let reshape121_out1 = slice168_out1.reshape(concat158_out1);
        let unsqueeze360_out1 = [sub21_out1 as i64];
        let unsqueeze361_out1 = [gather123_out1 as i64];
        let slice169_out1 = constant461_out1
            .slice(s![unsqueeze360_out1[0]..unsqueeze361_out1[0], ..]);
        let unsqueeze362_out1 = [gather122_out1 as i64];
        let constant1872_out1: [i64; 1] = [64i64];
        let constant1869_out1: [i64; 1] = [1i64];
        let constant1870_out1: [i64; 1] = [1i64];
        let concat159_out1: [i64; 4usize] = [
            &constant1869_out1[..],
            &constant1870_out1[..],
            &unsqueeze362_out1[..],
            &constant1872_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze363_out1 = [gather122_out1 as i64];
        let constant1876_out1: [i64; 1] = [64i64];
        let constant1873_out1: [i64; 1] = [1i64];
        let constant1874_out1: [i64; 1] = [1i64];
        let concat160_out1: [i64; 4usize] = [
            &constant1873_out1[..],
            &constant1874_out1[..],
            &unsqueeze363_out1[..],
            &constant1876_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape122_out1 = slice169_out1.reshape(concat159_out1);
        let unsqueeze364_out1 = [sub21_out1 as i64];
        let unsqueeze365_out1 = [gather123_out1 as i64];
        let slice170_out1 = constant474_out1
            .slice(s![unsqueeze364_out1[0]..unsqueeze365_out1[0], ..]);
        let reshape123_out1 = slice170_out1.reshape(concat160_out1);
        let cast194_out1 = reshape122_out1;
        let cast195_out1 = reshape123_out1;
        let mul154_out1 = squeeze55_out1.clone().mul(cast194_out1);
        let shape150_out1: [i64; 4] = {
            let axes = &squeeze55_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather124_out1 = shape150_out1[3] as i64;
        let constant1882_out1 = 2i64;
        let div75_out1 = gather124_out1 / constant1882_out1;
        let cast196_out1 = div75_out1;
        let cast197_out1 = cast196_out1;
        let unsqueeze366_out1 = [cast197_out1 as i64];
        let slice171_out1 = squeeze55_out1
            .clone()
            .slice(s![.., .., .., unsqueeze366_out1[0]..9223372036854775807]);
        let neg37_out1 = slice171_out1.neg();
        let unsqueeze367_out1 = [cast197_out1 as i64];
        let slice172_out1 = squeeze55_out1
            .slice(s![.., .., .., 0..unsqueeze367_out1[0]]);
        let concat161_out1 = burn::tensor::Tensor::cat(
            [neg37_out1, slice172_out1].into(),
            3,
        );
        let mul155_out1 = concat161_out1.mul(cast195_out1);
        let add122_out1 = mul154_out1.add(mul155_out1);
        let cast198_out1 = reshape120_out1;
        let cast199_out1 = reshape121_out1;
        let mul156_out1 = squeeze56_out1.clone().mul(cast198_out1);
        let shape151_out1: [i64; 4] = {
            let axes = &squeeze56_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather125_out1 = shape151_out1[3] as i64;
        let constant1892_out1 = 2i64;
        let div76_out1 = gather125_out1 / constant1892_out1;
        let cast200_out1 = div76_out1;
        let cast201_out1 = cast200_out1;
        let unsqueeze368_out1 = [cast201_out1 as i64];
        let slice173_out1 = squeeze56_out1
            .clone()
            .slice(s![.., .., .., unsqueeze368_out1[0]..9223372036854775807]);
        let neg38_out1 = slice173_out1.neg();
        let unsqueeze369_out1 = [cast201_out1 as i64];
        let slice174_out1 = squeeze56_out1
            .slice(s![.., .., .., 0..unsqueeze369_out1[0]]);
        let concat162_out1 = burn::tensor::Tensor::cat(
            [neg38_out1, slice174_out1].into(),
            3,
        );
        let mul157_out1 = concat162_out1.mul(cast199_out1);
        let add123_out1 = mul156_out1.add(mul157_out1);
        let shape152_out1: [i64; 4] = {
            let axes = &add122_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let slice175_out1: [i64; 1] = shape152_out1[3..4].try_into().unwrap();
        let cast202_out1 = {
            let shape_array = slice175_out1 as [i64; 1usize];
            let float_array: [f64; 1usize] = shape_array.map(|x| x as f64);
            Tensor::<
                1,
            >::from_data(
                TensorData::from(float_array),
                (&self.device, burn::tensor::DType::F32),
            )
        };
        let sqrt55_out1 = cast202_out1.sqrt();
        let constant1903_out1 = self.constant1903.val();
        let div77_out1 = constant1903_out1.div(sqrt55_out1);
        let cast203_out1 = div77_out1;
        let transpose56_out1 = add123_out1.permute([0, 1, 3, 2]);
        let sqrt56_out1 = cast203_out1.clone().sqrt();
        let mul158_out1 = add122_out1
            .mul((sqrt56_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let sqrt57_out1 = cast203_out1.sqrt();
        let mul159_out1 = transpose56_out1
            .mul((sqrt57_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let matmul111_out1 = mul158_out1.matmul(mul159_out1);
        let constant1905_out1 = self.constant1905.val();
        let constant1904_out1 = self.constant1904.val();
        let where43_out1 = constant1905_out1
            .unsqueeze_dims(&[0isize, 1isize, 2isize])
            .mask_where(
                less4_out1,
                constant1904_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let add124_out1 = matmul111_out1.add(where43_out1);
        let softmax19_out1 = burn::tensor::activation::softmax(add124_out1, 3);
        let isnan19_out1 = softmax19_out1.clone().is_nan();
        let constant1906_out1 = self.constant1906.val();
        let where44_out1 = softmax19_out1
            .mask_where(
                isnan19_out1,
                constant1906_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let matmul112_out1 = where44_out1.matmul(squeeze57_out1);
        let transpose57_out1 = matmul112_out1.permute([0, 2, 1, 3]);
        let unsqueeze370_out1 = [gather120_out1 as i64];
        let unsqueeze371_out1 = [gather121_out1 as i64];
        let constant1909_out1: [i64; 1] = [512i64];
        let concat163_out1: [i64; 3usize] = [
            &unsqueeze370_out1[..],
            &unsqueeze371_out1[..],
            &constant1909_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape124_out1 = transpose57_out1.reshape(concat163_out1);
        let linear75_out1 = self.linear75.forward(reshape124_out1);
        let add125_out1 = add121_out1.add(linear75_out1);
        add125_out1
    }
}
#[derive(Module, Debug)]
pub struct Submodule21 {
    layernormalization39: LayerNorm,
    linear76: Linear,
    constant1910: burn::module::Param<Tensor<1>>,
    constant1911: burn::module::Param<Tensor<1>>,
    constant1912: burn::module::Param<Tensor<1>>,
    linear77: Linear,
    layernormalization40: LayerNorm,
    linear78: Linear,
    constant1980: burn::module::Param<Tensor<1>>,
    constant1982: burn::module::Param<Tensor<1>>,
    constant1981: burn::module::Param<Tensor<1>>,
    constant1983: burn::module::Param<Tensor<1>>,
    linear79: Linear,
    #[module(skip)]
    device: Device,
}
impl Submodule21 {
    #[allow(unused_variables)]
    pub fn new(device: &Device) -> Self {
        let layernormalization39 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear76 = LinearConfig::new(512, 2048).with_bias(true).init(device);
        let constant1910: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1.4142135381698608f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1911: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1912: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([0.5f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let linear77 = LinearConfig::new(2048, 512).with_bias(true).init(device);
        let layernormalization40 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear78 = LinearConfig::new(512, 1536).with_bias(false).init(device);
        let constant1980: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1982: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1981: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1983: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let linear79 = LinearConfig::new(512, 512).with_bias(true).init(device);
        Self {
            layernormalization39,
            linear76,
            constant1910,
            constant1911,
            constant1912,
            linear77,
            layernormalization40,
            linear78,
            constant1980,
            constant1982,
            constant1981,
            constant1983,
            linear79,
            device: device.clone(),
        }
    }
    #[allow(clippy::let_and_return, clippy::approx_constant)]
    pub fn forward(
        &self,
        add125_out1: Tensor<3>,
        constant461_out1: Tensor<2>,
        constant474_out1: Tensor<2>,
        less4_out1: Tensor<4, Bool>,
    ) -> Tensor<3> {
        let layernormalization39_out1 = {
            let dtype = add125_out1.clone().dtype();
            self.layernormalization39
                .forward(add125_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let linear76_out1 = self.linear76.forward(layernormalization39_out1);
        let constant1910_out1 = self.constant1910.val();
        let div78_out1 = linear76_out1
            .clone()
            .div((constant1910_out1).unsqueeze_dims(&[0isize, 1isize]));
        let erf19_out1 = div78_out1.erf();
        let constant1911_out1 = self.constant1911.val();
        let add126_out1 = erf19_out1
            .add((constant1911_out1).unsqueeze_dims(&[0isize, 1isize]));
        let mul160_out1 = linear76_out1.mul(add126_out1);
        let constant1912_out1 = self.constant1912.val();
        let mul161_out1 = mul160_out1
            .mul((constant1912_out1).unsqueeze_dims(&[0isize, 1isize]));
        let linear77_out1 = self.linear77.forward(mul161_out1);
        let add127_out1 = add125_out1.add(linear77_out1);
        let layernormalization40_out1 = {
            let dtype = add127_out1.clone().dtype();
            self.layernormalization40
                .forward(add127_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let shape153_out1: [i64; 3] = {
            let axes = &layernormalization40_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather126_out1 = shape153_out1[0] as i64;
        let shape154_out1: [i64; 3] = {
            let axes = &layernormalization40_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather127_out1 = shape154_out1[1] as i64;
        let linear78_out1 = self.linear78.forward(layernormalization40_out1);
        let unsqueeze372_out1 = [gather126_out1 as i64];
        let unsqueeze373_out1 = [gather127_out1 as i64];
        let constant1919_out1: [i64; 1] = [64i64];
        let constant1917_out1: [i64; 1] = [3i64];
        let constant1918_out1: [i64; 1] = [8i64];
        let concat164_out1: [i64; 5usize] = [
            &unsqueeze372_out1[..],
            &unsqueeze373_out1[..],
            &constant1917_out1[..],
            &constant1918_out1[..],
            &constant1919_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape125_out1 = linear78_out1.reshape(concat164_out1);
        let transpose58_out1 = reshape125_out1.permute([2, 0, 3, 1, 4]);
        let split_tensors = transpose58_out1.split_with_sizes([1, 1, 1].into(), 0);
        let [split20_out1, split20_out2, split20_out3] = split_tensors
            .try_into()
            .unwrap();
        let squeeze58_out1 = split20_out1.squeeze_dims::<4>(&[0]);
        let squeeze59_out1 = split20_out2.squeeze_dims::<4>(&[0]);
        let squeeze60_out1 = split20_out3.squeeze_dims::<4>(&[0]);
        let shape155_out1: [i64; 4] = {
            let axes = &squeeze58_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather128_out1 = shape155_out1[2] as i64;
        let shape156_out1: [i64; 4] = {
            let axes = &squeeze59_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather129_out1 = shape156_out1[2] as i64;
        let sub22_out1 = gather129_out1 - gather128_out1;
        let unsqueeze374_out1 = [gather129_out1 as i64];
        let slice176_out1 = constant461_out1
            .clone()
            .slice(s![0..unsqueeze374_out1[0], ..]);
        let unsqueeze375_out1 = [gather129_out1 as i64];
        let constant1933_out1: [i64; 1] = [64i64];
        let constant1930_out1: [i64; 1] = [1i64];
        let constant1931_out1: [i64; 1] = [1i64];
        let concat165_out1: [i64; 4usize] = [
            &constant1930_out1[..],
            &constant1931_out1[..],
            &unsqueeze375_out1[..],
            &constant1933_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze376_out1 = [gather129_out1 as i64];
        let constant1937_out1: [i64; 1] = [64i64];
        let constant1934_out1: [i64; 1] = [1i64];
        let constant1935_out1: [i64; 1] = [1i64];
        let concat166_out1: [i64; 4usize] = [
            &constant1934_out1[..],
            &constant1935_out1[..],
            &unsqueeze376_out1[..],
            &constant1937_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape126_out1 = slice176_out1.reshape(concat165_out1);
        let unsqueeze377_out1 = [gather129_out1 as i64];
        let slice177_out1 = constant474_out1
            .clone()
            .slice(s![0..unsqueeze377_out1[0], ..]);
        let reshape127_out1 = slice177_out1.reshape(concat166_out1);
        let unsqueeze378_out1 = [sub22_out1 as i64];
        let unsqueeze379_out1 = [gather129_out1 as i64];
        let slice178_out1 = constant461_out1
            .slice(s![unsqueeze378_out1[0]..unsqueeze379_out1[0], ..]);
        let unsqueeze380_out1 = [gather128_out1 as i64];
        let constant1949_out1: [i64; 1] = [64i64];
        let constant1946_out1: [i64; 1] = [1i64];
        let constant1947_out1: [i64; 1] = [1i64];
        let concat167_out1: [i64; 4usize] = [
            &constant1946_out1[..],
            &constant1947_out1[..],
            &unsqueeze380_out1[..],
            &constant1949_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze381_out1 = [gather128_out1 as i64];
        let constant1953_out1: [i64; 1] = [64i64];
        let constant1950_out1: [i64; 1] = [1i64];
        let constant1951_out1: [i64; 1] = [1i64];
        let concat168_out1: [i64; 4usize] = [
            &constant1950_out1[..],
            &constant1951_out1[..],
            &unsqueeze381_out1[..],
            &constant1953_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape128_out1 = slice178_out1.reshape(concat167_out1);
        let unsqueeze382_out1 = [sub22_out1 as i64];
        let unsqueeze383_out1 = [gather129_out1 as i64];
        let slice179_out1 = constant474_out1
            .slice(s![unsqueeze382_out1[0]..unsqueeze383_out1[0], ..]);
        let reshape129_out1 = slice179_out1.reshape(concat168_out1);
        let cast204_out1 = reshape128_out1;
        let cast205_out1 = reshape129_out1;
        let mul162_out1 = squeeze58_out1.clone().mul(cast204_out1);
        let shape157_out1: [i64; 4] = {
            let axes = &squeeze58_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather130_out1 = shape157_out1[3] as i64;
        let constant1959_out1 = 2i64;
        let div79_out1 = gather130_out1 / constant1959_out1;
        let cast206_out1 = div79_out1;
        let cast207_out1 = cast206_out1;
        let unsqueeze384_out1 = [cast207_out1 as i64];
        let slice180_out1 = squeeze58_out1
            .clone()
            .slice(s![.., .., .., unsqueeze384_out1[0]..9223372036854775807]);
        let neg39_out1 = slice180_out1.neg();
        let unsqueeze385_out1 = [cast207_out1 as i64];
        let slice181_out1 = squeeze58_out1
            .slice(s![.., .., .., 0..unsqueeze385_out1[0]]);
        let concat169_out1 = burn::tensor::Tensor::cat(
            [neg39_out1, slice181_out1].into(),
            3,
        );
        let mul163_out1 = concat169_out1.mul(cast205_out1);
        let add128_out1 = mul162_out1.add(mul163_out1);
        let cast208_out1 = reshape126_out1;
        let cast209_out1 = reshape127_out1;
        let mul164_out1 = squeeze59_out1.clone().mul(cast208_out1);
        let shape158_out1: [i64; 4] = {
            let axes = &squeeze59_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather131_out1 = shape158_out1[3] as i64;
        let constant1969_out1 = 2i64;
        let div80_out1 = gather131_out1 / constant1969_out1;
        let cast210_out1 = div80_out1;
        let cast211_out1 = cast210_out1;
        let unsqueeze386_out1 = [cast211_out1 as i64];
        let slice182_out1 = squeeze59_out1
            .clone()
            .slice(s![.., .., .., unsqueeze386_out1[0]..9223372036854775807]);
        let neg40_out1 = slice182_out1.neg();
        let unsqueeze387_out1 = [cast211_out1 as i64];
        let slice183_out1 = squeeze59_out1
            .slice(s![.., .., .., 0..unsqueeze387_out1[0]]);
        let concat170_out1 = burn::tensor::Tensor::cat(
            [neg40_out1, slice183_out1].into(),
            3,
        );
        let mul165_out1 = concat170_out1.mul(cast209_out1);
        let add129_out1 = mul164_out1.add(mul165_out1);
        let shape159_out1: [i64; 4] = {
            let axes = &add128_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let slice184_out1: [i64; 1] = shape159_out1[3..4].try_into().unwrap();
        let cast212_out1 = {
            let shape_array = slice184_out1 as [i64; 1usize];
            let float_array: [f64; 1usize] = shape_array.map(|x| x as f64);
            Tensor::<
                1,
            >::from_data(
                TensorData::from(float_array),
                (&self.device, burn::tensor::DType::F32),
            )
        };
        let sqrt58_out1 = cast212_out1.sqrt();
        let constant1980_out1 = self.constant1980.val();
        let div81_out1 = constant1980_out1.div(sqrt58_out1);
        let cast213_out1 = div81_out1;
        let transpose59_out1 = add129_out1.permute([0, 1, 3, 2]);
        let sqrt59_out1 = cast213_out1.clone().sqrt();
        let mul166_out1 = add128_out1
            .mul((sqrt59_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let sqrt60_out1 = cast213_out1.sqrt();
        let mul167_out1 = transpose59_out1
            .mul((sqrt60_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let matmul117_out1 = mul166_out1.matmul(mul167_out1);
        let constant1982_out1 = self.constant1982.val();
        let constant1981_out1 = self.constant1981.val();
        let where45_out1 = constant1982_out1
            .unsqueeze_dims(&[0isize, 1isize, 2isize])
            .mask_where(
                less4_out1,
                constant1981_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let add130_out1 = matmul117_out1.add(where45_out1);
        let softmax20_out1 = burn::tensor::activation::softmax(add130_out1, 3);
        let isnan20_out1 = softmax20_out1.clone().is_nan();
        let constant1983_out1 = self.constant1983.val();
        let where46_out1 = softmax20_out1
            .mask_where(
                isnan20_out1,
                constant1983_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let matmul118_out1 = where46_out1.matmul(squeeze60_out1);
        let transpose60_out1 = matmul118_out1.permute([0, 2, 1, 3]);
        let unsqueeze388_out1 = [gather126_out1 as i64];
        let unsqueeze389_out1 = [gather127_out1 as i64];
        let constant1986_out1: [i64; 1] = [512i64];
        let concat171_out1: [i64; 3usize] = [
            &unsqueeze388_out1[..],
            &unsqueeze389_out1[..],
            &constant1986_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape130_out1 = transpose60_out1.reshape(concat171_out1);
        let linear79_out1 = self.linear79.forward(reshape130_out1);
        let add131_out1 = add127_out1.add(linear79_out1);
        add131_out1
    }
}
#[derive(Module, Debug)]
pub struct Submodule22 {
    layernormalization41: LayerNorm,
    linear80: Linear,
    constant1987: burn::module::Param<Tensor<1>>,
    constant1988: burn::module::Param<Tensor<1>>,
    constant1989: burn::module::Param<Tensor<1>>,
    linear81: Linear,
    layernormalization42: LayerNorm,
    linear82: Linear,
    constant2057: burn::module::Param<Tensor<1>>,
    constant2059: burn::module::Param<Tensor<1>>,
    constant2058: burn::module::Param<Tensor<1>>,
    constant2060: burn::module::Param<Tensor<1>>,
    linear83: Linear,
    #[module(skip)]
    device: Device,
}
impl Submodule22 {
    #[allow(unused_variables)]
    pub fn new(device: &Device) -> Self {
        let layernormalization41 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear80 = LinearConfig::new(512, 2048).with_bias(true).init(device);
        let constant1987: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1.4142135381698608f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1988: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant1989: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([0.5f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let linear81 = LinearConfig::new(2048, 512).with_bias(true).init(device);
        let layernormalization42 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear82 = LinearConfig::new(512, 1536).with_bias(false).init(device);
        let constant2057: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant2059: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant2058: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant2060: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let linear83 = LinearConfig::new(512, 512).with_bias(true).init(device);
        Self {
            layernormalization41,
            linear80,
            constant1987,
            constant1988,
            constant1989,
            linear81,
            layernormalization42,
            linear82,
            constant2057,
            constant2059,
            constant2058,
            constant2060,
            linear83,
            device: device.clone(),
        }
    }
    #[allow(clippy::let_and_return, clippy::approx_constant)]
    pub fn forward(
        &self,
        add131_out1: Tensor<3>,
        constant461_out1: Tensor<2>,
        constant474_out1: Tensor<2>,
        less4_out1: Tensor<4, Bool>,
    ) -> Tensor<3> {
        let layernormalization41_out1 = {
            let dtype = add131_out1.clone().dtype();
            self.layernormalization41
                .forward(add131_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let linear80_out1 = self.linear80.forward(layernormalization41_out1);
        let constant1987_out1 = self.constant1987.val();
        let div82_out1 = linear80_out1
            .clone()
            .div((constant1987_out1).unsqueeze_dims(&[0isize, 1isize]));
        let erf20_out1 = div82_out1.erf();
        let constant1988_out1 = self.constant1988.val();
        let add132_out1 = erf20_out1
            .add((constant1988_out1).unsqueeze_dims(&[0isize, 1isize]));
        let mul168_out1 = linear80_out1.mul(add132_out1);
        let constant1989_out1 = self.constant1989.val();
        let mul169_out1 = mul168_out1
            .mul((constant1989_out1).unsqueeze_dims(&[0isize, 1isize]));
        let linear81_out1 = self.linear81.forward(mul169_out1);
        let add133_out1 = add131_out1.add(linear81_out1);
        let layernormalization42_out1 = {
            let dtype = add133_out1.clone().dtype();
            self.layernormalization42
                .forward(add133_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let shape160_out1: [i64; 3] = {
            let axes = &layernormalization42_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather132_out1 = shape160_out1[0] as i64;
        let shape161_out1: [i64; 3] = {
            let axes = &layernormalization42_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather133_out1 = shape161_out1[1] as i64;
        let linear82_out1 = self.linear82.forward(layernormalization42_out1);
        let unsqueeze390_out1 = [gather132_out1 as i64];
        let unsqueeze391_out1 = [gather133_out1 as i64];
        let constant1996_out1: [i64; 1] = [64i64];
        let constant1994_out1: [i64; 1] = [3i64];
        let constant1995_out1: [i64; 1] = [8i64];
        let concat172_out1: [i64; 5usize] = [
            &unsqueeze390_out1[..],
            &unsqueeze391_out1[..],
            &constant1994_out1[..],
            &constant1995_out1[..],
            &constant1996_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape131_out1 = linear82_out1.reshape(concat172_out1);
        let transpose61_out1 = reshape131_out1.permute([2, 0, 3, 1, 4]);
        let split_tensors = transpose61_out1.split_with_sizes([1, 1, 1].into(), 0);
        let [split21_out1, split21_out2, split21_out3] = split_tensors
            .try_into()
            .unwrap();
        let squeeze61_out1 = split21_out1.squeeze_dims::<4>(&[0]);
        let squeeze62_out1 = split21_out2.squeeze_dims::<4>(&[0]);
        let squeeze63_out1 = split21_out3.squeeze_dims::<4>(&[0]);
        let shape162_out1: [i64; 4] = {
            let axes = &squeeze61_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather134_out1 = shape162_out1[2] as i64;
        let shape163_out1: [i64; 4] = {
            let axes = &squeeze62_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather135_out1 = shape163_out1[2] as i64;
        let sub23_out1 = gather135_out1 - gather134_out1;
        let unsqueeze392_out1 = [gather135_out1 as i64];
        let slice185_out1 = constant461_out1
            .clone()
            .slice(s![0..unsqueeze392_out1[0], ..]);
        let unsqueeze393_out1 = [gather135_out1 as i64];
        let constant2010_out1: [i64; 1] = [64i64];
        let constant2007_out1: [i64; 1] = [1i64];
        let constant2008_out1: [i64; 1] = [1i64];
        let concat173_out1: [i64; 4usize] = [
            &constant2007_out1[..],
            &constant2008_out1[..],
            &unsqueeze393_out1[..],
            &constant2010_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze394_out1 = [gather135_out1 as i64];
        let constant2014_out1: [i64; 1] = [64i64];
        let constant2011_out1: [i64; 1] = [1i64];
        let constant2012_out1: [i64; 1] = [1i64];
        let concat174_out1: [i64; 4usize] = [
            &constant2011_out1[..],
            &constant2012_out1[..],
            &unsqueeze394_out1[..],
            &constant2014_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape132_out1 = slice185_out1.reshape(concat173_out1);
        let unsqueeze395_out1 = [gather135_out1 as i64];
        let slice186_out1 = constant474_out1
            .clone()
            .slice(s![0..unsqueeze395_out1[0], ..]);
        let reshape133_out1 = slice186_out1.reshape(concat174_out1);
        let unsqueeze396_out1 = [sub23_out1 as i64];
        let unsqueeze397_out1 = [gather135_out1 as i64];
        let slice187_out1 = constant461_out1
            .slice(s![unsqueeze396_out1[0]..unsqueeze397_out1[0], ..]);
        let unsqueeze398_out1 = [gather134_out1 as i64];
        let constant2026_out1: [i64; 1] = [64i64];
        let constant2023_out1: [i64; 1] = [1i64];
        let constant2024_out1: [i64; 1] = [1i64];
        let concat175_out1: [i64; 4usize] = [
            &constant2023_out1[..],
            &constant2024_out1[..],
            &unsqueeze398_out1[..],
            &constant2026_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze399_out1 = [gather134_out1 as i64];
        let constant2030_out1: [i64; 1] = [64i64];
        let constant2027_out1: [i64; 1] = [1i64];
        let constant2028_out1: [i64; 1] = [1i64];
        let concat176_out1: [i64; 4usize] = [
            &constant2027_out1[..],
            &constant2028_out1[..],
            &unsqueeze399_out1[..],
            &constant2030_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape134_out1 = slice187_out1.reshape(concat175_out1);
        let unsqueeze400_out1 = [sub23_out1 as i64];
        let unsqueeze401_out1 = [gather135_out1 as i64];
        let slice188_out1 = constant474_out1
            .slice(s![unsqueeze400_out1[0]..unsqueeze401_out1[0], ..]);
        let reshape135_out1 = slice188_out1.reshape(concat176_out1);
        let cast214_out1 = reshape134_out1;
        let cast215_out1 = reshape135_out1;
        let mul170_out1 = squeeze61_out1.clone().mul(cast214_out1);
        let shape164_out1: [i64; 4] = {
            let axes = &squeeze61_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather136_out1 = shape164_out1[3] as i64;
        let constant2036_out1 = 2i64;
        let div83_out1 = gather136_out1 / constant2036_out1;
        let cast216_out1 = div83_out1;
        let cast217_out1 = cast216_out1;
        let unsqueeze402_out1 = [cast217_out1 as i64];
        let slice189_out1 = squeeze61_out1
            .clone()
            .slice(s![.., .., .., unsqueeze402_out1[0]..9223372036854775807]);
        let neg41_out1 = slice189_out1.neg();
        let unsqueeze403_out1 = [cast217_out1 as i64];
        let slice190_out1 = squeeze61_out1
            .slice(s![.., .., .., 0..unsqueeze403_out1[0]]);
        let concat177_out1 = burn::tensor::Tensor::cat(
            [neg41_out1, slice190_out1].into(),
            3,
        );
        let mul171_out1 = concat177_out1.mul(cast215_out1);
        let add134_out1 = mul170_out1.add(mul171_out1);
        let cast218_out1 = reshape132_out1;
        let cast219_out1 = reshape133_out1;
        let mul172_out1 = squeeze62_out1.clone().mul(cast218_out1);
        let shape165_out1: [i64; 4] = {
            let axes = &squeeze62_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather137_out1 = shape165_out1[3] as i64;
        let constant2046_out1 = 2i64;
        let div84_out1 = gather137_out1 / constant2046_out1;
        let cast220_out1 = div84_out1;
        let cast221_out1 = cast220_out1;
        let unsqueeze404_out1 = [cast221_out1 as i64];
        let slice191_out1 = squeeze62_out1
            .clone()
            .slice(s![.., .., .., unsqueeze404_out1[0]..9223372036854775807]);
        let neg42_out1 = slice191_out1.neg();
        let unsqueeze405_out1 = [cast221_out1 as i64];
        let slice192_out1 = squeeze62_out1
            .slice(s![.., .., .., 0..unsqueeze405_out1[0]]);
        let concat178_out1 = burn::tensor::Tensor::cat(
            [neg42_out1, slice192_out1].into(),
            3,
        );
        let mul173_out1 = concat178_out1.mul(cast219_out1);
        let add135_out1 = mul172_out1.add(mul173_out1);
        let shape166_out1: [i64; 4] = {
            let axes = &add134_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let slice193_out1: [i64; 1] = shape166_out1[3..4].try_into().unwrap();
        let cast222_out1 = {
            let shape_array = slice193_out1 as [i64; 1usize];
            let float_array: [f64; 1usize] = shape_array.map(|x| x as f64);
            Tensor::<
                1,
            >::from_data(
                TensorData::from(float_array),
                (&self.device, burn::tensor::DType::F32),
            )
        };
        let sqrt61_out1 = cast222_out1.sqrt();
        let constant2057_out1 = self.constant2057.val();
        let div85_out1 = constant2057_out1.div(sqrt61_out1);
        let cast223_out1 = div85_out1;
        let transpose62_out1 = add135_out1.permute([0, 1, 3, 2]);
        let sqrt62_out1 = cast223_out1.clone().sqrt();
        let mul174_out1 = add134_out1
            .mul((sqrt62_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let sqrt63_out1 = cast223_out1.sqrt();
        let mul175_out1 = transpose62_out1
            .mul((sqrt63_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let matmul123_out1 = mul174_out1.matmul(mul175_out1);
        let constant2059_out1 = self.constant2059.val();
        let constant2058_out1 = self.constant2058.val();
        let where47_out1 = constant2059_out1
            .unsqueeze_dims(&[0isize, 1isize, 2isize])
            .mask_where(
                less4_out1,
                constant2058_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let add136_out1 = matmul123_out1.add(where47_out1);
        let softmax21_out1 = burn::tensor::activation::softmax(add136_out1, 3);
        let isnan21_out1 = softmax21_out1.clone().is_nan();
        let constant2060_out1 = self.constant2060.val();
        let where48_out1 = softmax21_out1
            .mask_where(
                isnan21_out1,
                constant2060_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let matmul124_out1 = where48_out1.matmul(squeeze63_out1);
        let transpose63_out1 = matmul124_out1.permute([0, 2, 1, 3]);
        let unsqueeze406_out1 = [gather132_out1 as i64];
        let unsqueeze407_out1 = [gather133_out1 as i64];
        let constant2063_out1: [i64; 1] = [512i64];
        let concat179_out1: [i64; 3usize] = [
            &unsqueeze406_out1[..],
            &unsqueeze407_out1[..],
            &constant2063_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape136_out1 = transpose63_out1.reshape(concat179_out1);
        let linear83_out1 = self.linear83.forward(reshape136_out1);
        let add137_out1 = add133_out1.add(linear83_out1);
        add137_out1
    }
}
#[derive(Module, Debug)]
pub struct Submodule23 {
    layernormalization43: LayerNorm,
    linear84: Linear,
    constant2064: burn::module::Param<Tensor<1>>,
    constant2065: burn::module::Param<Tensor<1>>,
    constant2066: burn::module::Param<Tensor<1>>,
    linear85: Linear,
    layernormalization44: LayerNorm,
    linear86: Linear,
    constant2134: burn::module::Param<Tensor<1>>,
    constant2136: burn::module::Param<Tensor<1>>,
    constant2135: burn::module::Param<Tensor<1>>,
    constant2137: burn::module::Param<Tensor<1>>,
    linear87: Linear,
    #[module(skip)]
    device: Device,
}
impl Submodule23 {
    #[allow(unused_variables)]
    pub fn new(device: &Device) -> Self {
        let layernormalization43 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear84 = LinearConfig::new(512, 2048).with_bias(true).init(device);
        let constant2064: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1.4142135381698608f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant2065: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant2066: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([0.5f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let linear85 = LinearConfig::new(2048, 512).with_bias(true).init(device);
        let layernormalization44 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear86 = LinearConfig::new(512, 1536).with_bias(false).init(device);
        let constant2134: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant2136: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant2135: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant2137: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let linear87 = LinearConfig::new(512, 512).with_bias(true).init(device);
        Self {
            layernormalization43,
            linear84,
            constant2064,
            constant2065,
            constant2066,
            linear85,
            layernormalization44,
            linear86,
            constant2134,
            constant2136,
            constant2135,
            constant2137,
            linear87,
            device: device.clone(),
        }
    }
    #[allow(clippy::let_and_return, clippy::approx_constant)]
    pub fn forward(
        &self,
        add137_out1: Tensor<3>,
        constant461_out1: Tensor<2>,
        constant474_out1: Tensor<2>,
        less4_out1: Tensor<4, Bool>,
    ) -> Tensor<3> {
        let layernormalization43_out1 = {
            let dtype = add137_out1.clone().dtype();
            self.layernormalization43
                .forward(add137_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let linear84_out1 = self.linear84.forward(layernormalization43_out1);
        let constant2064_out1 = self.constant2064.val();
        let div86_out1 = linear84_out1
            .clone()
            .div((constant2064_out1).unsqueeze_dims(&[0isize, 1isize]));
        let erf21_out1 = div86_out1.erf();
        let constant2065_out1 = self.constant2065.val();
        let add138_out1 = erf21_out1
            .add((constant2065_out1).unsqueeze_dims(&[0isize, 1isize]));
        let mul176_out1 = linear84_out1.mul(add138_out1);
        let constant2066_out1 = self.constant2066.val();
        let mul177_out1 = mul176_out1
            .mul((constant2066_out1).unsqueeze_dims(&[0isize, 1isize]));
        let linear85_out1 = self.linear85.forward(mul177_out1);
        let add139_out1 = add137_out1.add(linear85_out1);
        let layernormalization44_out1 = {
            let dtype = add139_out1.clone().dtype();
            self.layernormalization44
                .forward(add139_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let shape167_out1: [i64; 3] = {
            let axes = &layernormalization44_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather138_out1 = shape167_out1[0] as i64;
        let shape168_out1: [i64; 3] = {
            let axes = &layernormalization44_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather139_out1 = shape168_out1[1] as i64;
        let linear86_out1 = self.linear86.forward(layernormalization44_out1);
        let unsqueeze408_out1 = [gather138_out1 as i64];
        let unsqueeze409_out1 = [gather139_out1 as i64];
        let constant2073_out1: [i64; 1] = [64i64];
        let constant2071_out1: [i64; 1] = [3i64];
        let constant2072_out1: [i64; 1] = [8i64];
        let concat180_out1: [i64; 5usize] = [
            &unsqueeze408_out1[..],
            &unsqueeze409_out1[..],
            &constant2071_out1[..],
            &constant2072_out1[..],
            &constant2073_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape137_out1 = linear86_out1.reshape(concat180_out1);
        let transpose64_out1 = reshape137_out1.permute([2, 0, 3, 1, 4]);
        let split_tensors = transpose64_out1.split_with_sizes([1, 1, 1].into(), 0);
        let [split22_out1, split22_out2, split22_out3] = split_tensors
            .try_into()
            .unwrap();
        let squeeze64_out1 = split22_out1.squeeze_dims::<4>(&[0]);
        let squeeze65_out1 = split22_out2.squeeze_dims::<4>(&[0]);
        let squeeze66_out1 = split22_out3.squeeze_dims::<4>(&[0]);
        let shape169_out1: [i64; 4] = {
            let axes = &squeeze64_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather140_out1 = shape169_out1[2] as i64;
        let shape170_out1: [i64; 4] = {
            let axes = &squeeze65_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather141_out1 = shape170_out1[2] as i64;
        let sub24_out1 = gather141_out1 - gather140_out1;
        let unsqueeze410_out1 = [gather141_out1 as i64];
        let slice194_out1 = constant461_out1
            .clone()
            .slice(s![0..unsqueeze410_out1[0], ..]);
        let unsqueeze411_out1 = [gather141_out1 as i64];
        let constant2087_out1: [i64; 1] = [64i64];
        let constant2084_out1: [i64; 1] = [1i64];
        let constant2085_out1: [i64; 1] = [1i64];
        let concat181_out1: [i64; 4usize] = [
            &constant2084_out1[..],
            &constant2085_out1[..],
            &unsqueeze411_out1[..],
            &constant2087_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze412_out1 = [gather141_out1 as i64];
        let constant2091_out1: [i64; 1] = [64i64];
        let constant2088_out1: [i64; 1] = [1i64];
        let constant2089_out1: [i64; 1] = [1i64];
        let concat182_out1: [i64; 4usize] = [
            &constant2088_out1[..],
            &constant2089_out1[..],
            &unsqueeze412_out1[..],
            &constant2091_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape138_out1 = slice194_out1.reshape(concat181_out1);
        let unsqueeze413_out1 = [gather141_out1 as i64];
        let slice195_out1 = constant474_out1
            .clone()
            .slice(s![0..unsqueeze413_out1[0], ..]);
        let reshape139_out1 = slice195_out1.reshape(concat182_out1);
        let unsqueeze414_out1 = [sub24_out1 as i64];
        let unsqueeze415_out1 = [gather141_out1 as i64];
        let slice196_out1 = constant461_out1
            .slice(s![unsqueeze414_out1[0]..unsqueeze415_out1[0], ..]);
        let unsqueeze416_out1 = [gather140_out1 as i64];
        let constant2103_out1: [i64; 1] = [64i64];
        let constant2100_out1: [i64; 1] = [1i64];
        let constant2101_out1: [i64; 1] = [1i64];
        let concat183_out1: [i64; 4usize] = [
            &constant2100_out1[..],
            &constant2101_out1[..],
            &unsqueeze416_out1[..],
            &constant2103_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze417_out1 = [gather140_out1 as i64];
        let constant2107_out1: [i64; 1] = [64i64];
        let constant2104_out1: [i64; 1] = [1i64];
        let constant2105_out1: [i64; 1] = [1i64];
        let concat184_out1: [i64; 4usize] = [
            &constant2104_out1[..],
            &constant2105_out1[..],
            &unsqueeze417_out1[..],
            &constant2107_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape140_out1 = slice196_out1.reshape(concat183_out1);
        let unsqueeze418_out1 = [sub24_out1 as i64];
        let unsqueeze419_out1 = [gather141_out1 as i64];
        let slice197_out1 = constant474_out1
            .slice(s![unsqueeze418_out1[0]..unsqueeze419_out1[0], ..]);
        let reshape141_out1 = slice197_out1.reshape(concat184_out1);
        let cast224_out1 = reshape140_out1;
        let cast225_out1 = reshape141_out1;
        let mul178_out1 = squeeze64_out1.clone().mul(cast224_out1);
        let shape171_out1: [i64; 4] = {
            let axes = &squeeze64_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather142_out1 = shape171_out1[3] as i64;
        let constant2113_out1 = 2i64;
        let div87_out1 = gather142_out1 / constant2113_out1;
        let cast226_out1 = div87_out1;
        let cast227_out1 = cast226_out1;
        let unsqueeze420_out1 = [cast227_out1 as i64];
        let slice198_out1 = squeeze64_out1
            .clone()
            .slice(s![.., .., .., unsqueeze420_out1[0]..9223372036854775807]);
        let neg43_out1 = slice198_out1.neg();
        let unsqueeze421_out1 = [cast227_out1 as i64];
        let slice199_out1 = squeeze64_out1
            .slice(s![.., .., .., 0..unsqueeze421_out1[0]]);
        let concat185_out1 = burn::tensor::Tensor::cat(
            [neg43_out1, slice199_out1].into(),
            3,
        );
        let mul179_out1 = concat185_out1.mul(cast225_out1);
        let add140_out1 = mul178_out1.add(mul179_out1);
        let cast228_out1 = reshape138_out1;
        let cast229_out1 = reshape139_out1;
        let mul180_out1 = squeeze65_out1.clone().mul(cast228_out1);
        let shape172_out1: [i64; 4] = {
            let axes = &squeeze65_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather143_out1 = shape172_out1[3] as i64;
        let constant2123_out1 = 2i64;
        let div88_out1 = gather143_out1 / constant2123_out1;
        let cast230_out1 = div88_out1;
        let cast231_out1 = cast230_out1;
        let unsqueeze422_out1 = [cast231_out1 as i64];
        let slice200_out1 = squeeze65_out1
            .clone()
            .slice(s![.., .., .., unsqueeze422_out1[0]..9223372036854775807]);
        let neg44_out1 = slice200_out1.neg();
        let unsqueeze423_out1 = [cast231_out1 as i64];
        let slice201_out1 = squeeze65_out1
            .slice(s![.., .., .., 0..unsqueeze423_out1[0]]);
        let concat186_out1 = burn::tensor::Tensor::cat(
            [neg44_out1, slice201_out1].into(),
            3,
        );
        let mul181_out1 = concat186_out1.mul(cast229_out1);
        let add141_out1 = mul180_out1.add(mul181_out1);
        let shape173_out1: [i64; 4] = {
            let axes = &add140_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let slice202_out1: [i64; 1] = shape173_out1[3..4].try_into().unwrap();
        let cast232_out1 = {
            let shape_array = slice202_out1 as [i64; 1usize];
            let float_array: [f64; 1usize] = shape_array.map(|x| x as f64);
            Tensor::<
                1,
            >::from_data(
                TensorData::from(float_array),
                (&self.device, burn::tensor::DType::F32),
            )
        };
        let sqrt64_out1 = cast232_out1.sqrt();
        let constant2134_out1 = self.constant2134.val();
        let div89_out1 = constant2134_out1.div(sqrt64_out1);
        let cast233_out1 = div89_out1;
        let transpose65_out1 = add141_out1.permute([0, 1, 3, 2]);
        let sqrt65_out1 = cast233_out1.clone().sqrt();
        let mul182_out1 = add140_out1
            .mul((sqrt65_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let sqrt66_out1 = cast233_out1.sqrt();
        let mul183_out1 = transpose65_out1
            .mul((sqrt66_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let matmul129_out1 = mul182_out1.matmul(mul183_out1);
        let constant2136_out1 = self.constant2136.val();
        let constant2135_out1 = self.constant2135.val();
        let where49_out1 = constant2136_out1
            .unsqueeze_dims(&[0isize, 1isize, 2isize])
            .mask_where(
                less4_out1,
                constant2135_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let add142_out1 = matmul129_out1.add(where49_out1);
        let softmax22_out1 = burn::tensor::activation::softmax(add142_out1, 3);
        let isnan22_out1 = softmax22_out1.clone().is_nan();
        let constant2137_out1 = self.constant2137.val();
        let where50_out1 = softmax22_out1
            .mask_where(
                isnan22_out1,
                constant2137_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let matmul130_out1 = where50_out1.matmul(squeeze66_out1);
        let transpose66_out1 = matmul130_out1.permute([0, 2, 1, 3]);
        let unsqueeze424_out1 = [gather138_out1 as i64];
        let unsqueeze425_out1 = [gather139_out1 as i64];
        let constant2140_out1: [i64; 1] = [512i64];
        let concat187_out1: [i64; 3usize] = [
            &unsqueeze424_out1[..],
            &unsqueeze425_out1[..],
            &constant2140_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape142_out1 = transpose66_out1.reshape(concat187_out1);
        let linear87_out1 = self.linear87.forward(reshape142_out1);
        let add143_out1 = add139_out1.add(linear87_out1);
        add143_out1
    }
}
#[derive(Module, Debug)]
pub struct Submodule24 {
    layernormalization45: LayerNorm,
    linear88: Linear,
    constant2141: burn::module::Param<Tensor<1>>,
    constant2142: burn::module::Param<Tensor<1>>,
    constant2143: burn::module::Param<Tensor<1>>,
    linear89: Linear,
    layernormalization46: LayerNorm,
    linear90: Linear,
    constant2211: burn::module::Param<Tensor<1>>,
    constant2213: burn::module::Param<Tensor<1>>,
    constant2212: burn::module::Param<Tensor<1>>,
    constant2214: burn::module::Param<Tensor<1>>,
    linear91: Linear,
    #[module(skip)]
    device: Device,
}
impl Submodule24 {
    #[allow(unused_variables)]
    pub fn new(device: &Device) -> Self {
        let layernormalization45 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear88 = LinearConfig::new(512, 2048).with_bias(true).init(device);
        let constant2141: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1.4142135381698608f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant2142: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant2143: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([0.5f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let linear89 = LinearConfig::new(2048, 512).with_bias(true).init(device);
        let layernormalization46 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear90 = LinearConfig::new(512, 1536).with_bias(false).init(device);
        let constant2211: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant2213: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant2212: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant2214: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let linear91 = LinearConfig::new(512, 512).with_bias(true).init(device);
        Self {
            layernormalization45,
            linear88,
            constant2141,
            constant2142,
            constant2143,
            linear89,
            layernormalization46,
            linear90,
            constant2211,
            constant2213,
            constant2212,
            constant2214,
            linear91,
            device: device.clone(),
        }
    }
    #[allow(clippy::let_and_return, clippy::approx_constant)]
    pub fn forward(
        &self,
        add143_out1: Tensor<3>,
        constant461_out1: Tensor<2>,
        constant474_out1: Tensor<2>,
        less4_out1: Tensor<4, Bool>,
    ) -> Tensor<3> {
        let layernormalization45_out1 = {
            let dtype = add143_out1.clone().dtype();
            self.layernormalization45
                .forward(add143_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let linear88_out1 = self.linear88.forward(layernormalization45_out1);
        let constant2141_out1 = self.constant2141.val();
        let div90_out1 = linear88_out1
            .clone()
            .div((constant2141_out1).unsqueeze_dims(&[0isize, 1isize]));
        let erf22_out1 = div90_out1.erf();
        let constant2142_out1 = self.constant2142.val();
        let add144_out1 = erf22_out1
            .add((constant2142_out1).unsqueeze_dims(&[0isize, 1isize]));
        let mul184_out1 = linear88_out1.mul(add144_out1);
        let constant2143_out1 = self.constant2143.val();
        let mul185_out1 = mul184_out1
            .mul((constant2143_out1).unsqueeze_dims(&[0isize, 1isize]));
        let linear89_out1 = self.linear89.forward(mul185_out1);
        let add145_out1 = add143_out1.add(linear89_out1);
        let layernormalization46_out1 = {
            let dtype = add145_out1.clone().dtype();
            self.layernormalization46
                .forward(add145_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let shape174_out1: [i64; 3] = {
            let axes = &layernormalization46_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather144_out1 = shape174_out1[0] as i64;
        let shape175_out1: [i64; 3] = {
            let axes = &layernormalization46_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather145_out1 = shape175_out1[1] as i64;
        let linear90_out1 = self.linear90.forward(layernormalization46_out1);
        let unsqueeze426_out1 = [gather144_out1 as i64];
        let unsqueeze427_out1 = [gather145_out1 as i64];
        let constant2150_out1: [i64; 1] = [64i64];
        let constant2148_out1: [i64; 1] = [3i64];
        let constant2149_out1: [i64; 1] = [8i64];
        let concat188_out1: [i64; 5usize] = [
            &unsqueeze426_out1[..],
            &unsqueeze427_out1[..],
            &constant2148_out1[..],
            &constant2149_out1[..],
            &constant2150_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape143_out1 = linear90_out1.reshape(concat188_out1);
        let transpose67_out1 = reshape143_out1.permute([2, 0, 3, 1, 4]);
        let split_tensors = transpose67_out1.split_with_sizes([1, 1, 1].into(), 0);
        let [split23_out1, split23_out2, split23_out3] = split_tensors
            .try_into()
            .unwrap();
        let squeeze67_out1 = split23_out1.squeeze_dims::<4>(&[0]);
        let squeeze68_out1 = split23_out2.squeeze_dims::<4>(&[0]);
        let squeeze69_out1 = split23_out3.squeeze_dims::<4>(&[0]);
        let shape176_out1: [i64; 4] = {
            let axes = &squeeze67_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather146_out1 = shape176_out1[2] as i64;
        let shape177_out1: [i64; 4] = {
            let axes = &squeeze68_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather147_out1 = shape177_out1[2] as i64;
        let sub25_out1 = gather147_out1 - gather146_out1;
        let unsqueeze428_out1 = [gather147_out1 as i64];
        let slice203_out1 = constant461_out1
            .clone()
            .slice(s![0..unsqueeze428_out1[0], ..]);
        let unsqueeze429_out1 = [gather147_out1 as i64];
        let constant2164_out1: [i64; 1] = [64i64];
        let constant2161_out1: [i64; 1] = [1i64];
        let constant2162_out1: [i64; 1] = [1i64];
        let concat189_out1: [i64; 4usize] = [
            &constant2161_out1[..],
            &constant2162_out1[..],
            &unsqueeze429_out1[..],
            &constant2164_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze430_out1 = [gather147_out1 as i64];
        let constant2168_out1: [i64; 1] = [64i64];
        let constant2165_out1: [i64; 1] = [1i64];
        let constant2166_out1: [i64; 1] = [1i64];
        let concat190_out1: [i64; 4usize] = [
            &constant2165_out1[..],
            &constant2166_out1[..],
            &unsqueeze430_out1[..],
            &constant2168_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape144_out1 = slice203_out1.reshape(concat189_out1);
        let unsqueeze431_out1 = [gather147_out1 as i64];
        let slice204_out1 = constant474_out1
            .clone()
            .slice(s![0..unsqueeze431_out1[0], ..]);
        let reshape145_out1 = slice204_out1.reshape(concat190_out1);
        let unsqueeze432_out1 = [sub25_out1 as i64];
        let unsqueeze433_out1 = [gather147_out1 as i64];
        let slice205_out1 = constant461_out1
            .slice(s![unsqueeze432_out1[0]..unsqueeze433_out1[0], ..]);
        let unsqueeze434_out1 = [gather146_out1 as i64];
        let constant2180_out1: [i64; 1] = [64i64];
        let constant2177_out1: [i64; 1] = [1i64];
        let constant2178_out1: [i64; 1] = [1i64];
        let concat191_out1: [i64; 4usize] = [
            &constant2177_out1[..],
            &constant2178_out1[..],
            &unsqueeze434_out1[..],
            &constant2180_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze435_out1 = [gather146_out1 as i64];
        let constant2184_out1: [i64; 1] = [64i64];
        let constant2181_out1: [i64; 1] = [1i64];
        let constant2182_out1: [i64; 1] = [1i64];
        let concat192_out1: [i64; 4usize] = [
            &constant2181_out1[..],
            &constant2182_out1[..],
            &unsqueeze435_out1[..],
            &constant2184_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape146_out1 = slice205_out1.reshape(concat191_out1);
        let unsqueeze436_out1 = [sub25_out1 as i64];
        let unsqueeze437_out1 = [gather147_out1 as i64];
        let slice206_out1 = constant474_out1
            .slice(s![unsqueeze436_out1[0]..unsqueeze437_out1[0], ..]);
        let reshape147_out1 = slice206_out1.reshape(concat192_out1);
        let cast234_out1 = reshape146_out1;
        let cast235_out1 = reshape147_out1;
        let mul186_out1 = squeeze67_out1.clone().mul(cast234_out1);
        let shape178_out1: [i64; 4] = {
            let axes = &squeeze67_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather148_out1 = shape178_out1[3] as i64;
        let constant2190_out1 = 2i64;
        let div91_out1 = gather148_out1 / constant2190_out1;
        let cast236_out1 = div91_out1;
        let cast237_out1 = cast236_out1;
        let unsqueeze438_out1 = [cast237_out1 as i64];
        let slice207_out1 = squeeze67_out1
            .clone()
            .slice(s![.., .., .., unsqueeze438_out1[0]..9223372036854775807]);
        let neg45_out1 = slice207_out1.neg();
        let unsqueeze439_out1 = [cast237_out1 as i64];
        let slice208_out1 = squeeze67_out1
            .slice(s![.., .., .., 0..unsqueeze439_out1[0]]);
        let concat193_out1 = burn::tensor::Tensor::cat(
            [neg45_out1, slice208_out1].into(),
            3,
        );
        let mul187_out1 = concat193_out1.mul(cast235_out1);
        let add146_out1 = mul186_out1.add(mul187_out1);
        let cast238_out1 = reshape144_out1;
        let cast239_out1 = reshape145_out1;
        let mul188_out1 = squeeze68_out1.clone().mul(cast238_out1);
        let shape179_out1: [i64; 4] = {
            let axes = &squeeze68_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather149_out1 = shape179_out1[3] as i64;
        let constant2200_out1 = 2i64;
        let div92_out1 = gather149_out1 / constant2200_out1;
        let cast240_out1 = div92_out1;
        let cast241_out1 = cast240_out1;
        let unsqueeze440_out1 = [cast241_out1 as i64];
        let slice209_out1 = squeeze68_out1
            .clone()
            .slice(s![.., .., .., unsqueeze440_out1[0]..9223372036854775807]);
        let neg46_out1 = slice209_out1.neg();
        let unsqueeze441_out1 = [cast241_out1 as i64];
        let slice210_out1 = squeeze68_out1
            .slice(s![.., .., .., 0..unsqueeze441_out1[0]]);
        let concat194_out1 = burn::tensor::Tensor::cat(
            [neg46_out1, slice210_out1].into(),
            3,
        );
        let mul189_out1 = concat194_out1.mul(cast239_out1);
        let add147_out1 = mul188_out1.add(mul189_out1);
        let shape180_out1: [i64; 4] = {
            let axes = &add146_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let slice211_out1: [i64; 1] = shape180_out1[3..4].try_into().unwrap();
        let cast242_out1 = {
            let shape_array = slice211_out1 as [i64; 1usize];
            let float_array: [f64; 1usize] = shape_array.map(|x| x as f64);
            Tensor::<
                1,
            >::from_data(
                TensorData::from(float_array),
                (&self.device, burn::tensor::DType::F32),
            )
        };
        let sqrt67_out1 = cast242_out1.sqrt();
        let constant2211_out1 = self.constant2211.val();
        let div93_out1 = constant2211_out1.div(sqrt67_out1);
        let cast243_out1 = div93_out1;
        let transpose68_out1 = add147_out1.permute([0, 1, 3, 2]);
        let sqrt68_out1 = cast243_out1.clone().sqrt();
        let mul190_out1 = add146_out1
            .mul((sqrt68_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let sqrt69_out1 = cast243_out1.sqrt();
        let mul191_out1 = transpose68_out1
            .mul((sqrt69_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let matmul135_out1 = mul190_out1.matmul(mul191_out1);
        let constant2213_out1 = self.constant2213.val();
        let constant2212_out1 = self.constant2212.val();
        let where51_out1 = constant2213_out1
            .unsqueeze_dims(&[0isize, 1isize, 2isize])
            .mask_where(
                less4_out1,
                constant2212_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let add148_out1 = matmul135_out1.add(where51_out1);
        let softmax23_out1 = burn::tensor::activation::softmax(add148_out1, 3);
        let isnan23_out1 = softmax23_out1.clone().is_nan();
        let constant2214_out1 = self.constant2214.val();
        let where52_out1 = softmax23_out1
            .mask_where(
                isnan23_out1,
                constant2214_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let matmul136_out1 = where52_out1.matmul(squeeze69_out1);
        let transpose69_out1 = matmul136_out1.permute([0, 2, 1, 3]);
        let unsqueeze442_out1 = [gather144_out1 as i64];
        let unsqueeze443_out1 = [gather145_out1 as i64];
        let constant2217_out1: [i64; 1] = [512i64];
        let concat195_out1: [i64; 3usize] = [
            &unsqueeze442_out1[..],
            &unsqueeze443_out1[..],
            &constant2217_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape148_out1 = transpose69_out1.reshape(concat195_out1);
        let linear91_out1 = self.linear91.forward(reshape148_out1);
        let add149_out1 = add145_out1.add(linear91_out1);
        add149_out1
    }
}
#[derive(Module, Debug)]
pub struct Submodule25 {
    layernormalization47: LayerNorm,
    linear92: Linear,
    constant2218: burn::module::Param<Tensor<1>>,
    constant2219: burn::module::Param<Tensor<1>>,
    constant2220: burn::module::Param<Tensor<1>>,
    linear93: Linear,
    layernormalization48: LayerNorm,
    linear94: Linear,
    constant2288: burn::module::Param<Tensor<1>>,
    constant2290: burn::module::Param<Tensor<1>>,
    constant2289: burn::module::Param<Tensor<1>>,
    constant2291: burn::module::Param<Tensor<1>>,
    linear95: Linear,
    #[module(skip)]
    device: Device,
}
impl Submodule25 {
    #[allow(unused_variables)]
    pub fn new(device: &Device) -> Self {
        let layernormalization47 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear92 = LinearConfig::new(512, 2048).with_bias(true).init(device);
        let constant2218: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1.4142135381698608f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant2219: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant2220: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([0.5f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let linear93 = LinearConfig::new(2048, 512).with_bias(true).init(device);
        let layernormalization48 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear94 = LinearConfig::new(512, 1536).with_bias(false).init(device);
        let constant2288: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant2290: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant2289: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant2291: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let linear95 = LinearConfig::new(512, 512).with_bias(true).init(device);
        Self {
            layernormalization47,
            linear92,
            constant2218,
            constant2219,
            constant2220,
            linear93,
            layernormalization48,
            linear94,
            constant2288,
            constant2290,
            constant2289,
            constant2291,
            linear95,
            device: device.clone(),
        }
    }
    #[allow(clippy::let_and_return, clippy::approx_constant)]
    pub fn forward(
        &self,
        add149_out1: Tensor<3>,
        constant461_out1: Tensor<2>,
        constant474_out1: Tensor<2>,
        less4_out1: Tensor<4, Bool>,
    ) -> Tensor<3> {
        let layernormalization47_out1 = {
            let dtype = add149_out1.clone().dtype();
            self.layernormalization47
                .forward(add149_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let linear92_out1 = self.linear92.forward(layernormalization47_out1);
        let constant2218_out1 = self.constant2218.val();
        let div94_out1 = linear92_out1
            .clone()
            .div((constant2218_out1).unsqueeze_dims(&[0isize, 1isize]));
        let erf23_out1 = div94_out1.erf();
        let constant2219_out1 = self.constant2219.val();
        let add150_out1 = erf23_out1
            .add((constant2219_out1).unsqueeze_dims(&[0isize, 1isize]));
        let mul192_out1 = linear92_out1.mul(add150_out1);
        let constant2220_out1 = self.constant2220.val();
        let mul193_out1 = mul192_out1
            .mul((constant2220_out1).unsqueeze_dims(&[0isize, 1isize]));
        let linear93_out1 = self.linear93.forward(mul193_out1);
        let add151_out1 = add149_out1.add(linear93_out1);
        let layernormalization48_out1 = {
            let dtype = add151_out1.clone().dtype();
            self.layernormalization48
                .forward(add151_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let shape181_out1: [i64; 3] = {
            let axes = &layernormalization48_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather150_out1 = shape181_out1[0] as i64;
        let shape182_out1: [i64; 3] = {
            let axes = &layernormalization48_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather151_out1 = shape182_out1[1] as i64;
        let linear94_out1 = self.linear94.forward(layernormalization48_out1);
        let unsqueeze444_out1 = [gather150_out1 as i64];
        let unsqueeze445_out1 = [gather151_out1 as i64];
        let constant2227_out1: [i64; 1] = [64i64];
        let constant2225_out1: [i64; 1] = [3i64];
        let constant2226_out1: [i64; 1] = [8i64];
        let concat196_out1: [i64; 5usize] = [
            &unsqueeze444_out1[..],
            &unsqueeze445_out1[..],
            &constant2225_out1[..],
            &constant2226_out1[..],
            &constant2227_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape149_out1 = linear94_out1.reshape(concat196_out1);
        let transpose70_out1 = reshape149_out1.permute([2, 0, 3, 1, 4]);
        let split_tensors = transpose70_out1.split_with_sizes([1, 1, 1].into(), 0);
        let [split24_out1, split24_out2, split24_out3] = split_tensors
            .try_into()
            .unwrap();
        let squeeze70_out1 = split24_out1.squeeze_dims::<4>(&[0]);
        let squeeze71_out1 = split24_out2.squeeze_dims::<4>(&[0]);
        let squeeze72_out1 = split24_out3.squeeze_dims::<4>(&[0]);
        let shape183_out1: [i64; 4] = {
            let axes = &squeeze70_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather152_out1 = shape183_out1[2] as i64;
        let shape184_out1: [i64; 4] = {
            let axes = &squeeze71_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather153_out1 = shape184_out1[2] as i64;
        let sub26_out1 = gather153_out1 - gather152_out1;
        let unsqueeze446_out1 = [gather153_out1 as i64];
        let slice212_out1 = constant461_out1
            .clone()
            .slice(s![0..unsqueeze446_out1[0], ..]);
        let unsqueeze447_out1 = [gather153_out1 as i64];
        let constant2241_out1: [i64; 1] = [64i64];
        let constant2238_out1: [i64; 1] = [1i64];
        let constant2239_out1: [i64; 1] = [1i64];
        let concat197_out1: [i64; 4usize] = [
            &constant2238_out1[..],
            &constant2239_out1[..],
            &unsqueeze447_out1[..],
            &constant2241_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze448_out1 = [gather153_out1 as i64];
        let constant2245_out1: [i64; 1] = [64i64];
        let constant2242_out1: [i64; 1] = [1i64];
        let constant2243_out1: [i64; 1] = [1i64];
        let concat198_out1: [i64; 4usize] = [
            &constant2242_out1[..],
            &constant2243_out1[..],
            &unsqueeze448_out1[..],
            &constant2245_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape150_out1 = slice212_out1.reshape(concat197_out1);
        let unsqueeze449_out1 = [gather153_out1 as i64];
        let slice213_out1 = constant474_out1
            .clone()
            .slice(s![0..unsqueeze449_out1[0], ..]);
        let reshape151_out1 = slice213_out1.reshape(concat198_out1);
        let unsqueeze450_out1 = [sub26_out1 as i64];
        let unsqueeze451_out1 = [gather153_out1 as i64];
        let slice214_out1 = constant461_out1
            .slice(s![unsqueeze450_out1[0]..unsqueeze451_out1[0], ..]);
        let unsqueeze452_out1 = [gather152_out1 as i64];
        let constant2257_out1: [i64; 1] = [64i64];
        let constant2254_out1: [i64; 1] = [1i64];
        let constant2255_out1: [i64; 1] = [1i64];
        let concat199_out1: [i64; 4usize] = [
            &constant2254_out1[..],
            &constant2255_out1[..],
            &unsqueeze452_out1[..],
            &constant2257_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze453_out1 = [gather152_out1 as i64];
        let constant2261_out1: [i64; 1] = [64i64];
        let constant2258_out1: [i64; 1] = [1i64];
        let constant2259_out1: [i64; 1] = [1i64];
        let concat200_out1: [i64; 4usize] = [
            &constant2258_out1[..],
            &constant2259_out1[..],
            &unsqueeze453_out1[..],
            &constant2261_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape152_out1 = slice214_out1.reshape(concat199_out1);
        let unsqueeze454_out1 = [sub26_out1 as i64];
        let unsqueeze455_out1 = [gather153_out1 as i64];
        let slice215_out1 = constant474_out1
            .slice(s![unsqueeze454_out1[0]..unsqueeze455_out1[0], ..]);
        let reshape153_out1 = slice215_out1.reshape(concat200_out1);
        let cast244_out1 = reshape152_out1;
        let cast245_out1 = reshape153_out1;
        let mul194_out1 = squeeze70_out1.clone().mul(cast244_out1);
        let shape185_out1: [i64; 4] = {
            let axes = &squeeze70_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather154_out1 = shape185_out1[3] as i64;
        let constant2267_out1 = 2i64;
        let div95_out1 = gather154_out1 / constant2267_out1;
        let cast246_out1 = div95_out1;
        let cast247_out1 = cast246_out1;
        let unsqueeze456_out1 = [cast247_out1 as i64];
        let slice216_out1 = squeeze70_out1
            .clone()
            .slice(s![.., .., .., unsqueeze456_out1[0]..9223372036854775807]);
        let neg47_out1 = slice216_out1.neg();
        let unsqueeze457_out1 = [cast247_out1 as i64];
        let slice217_out1 = squeeze70_out1
            .slice(s![.., .., .., 0..unsqueeze457_out1[0]]);
        let concat201_out1 = burn::tensor::Tensor::cat(
            [neg47_out1, slice217_out1].into(),
            3,
        );
        let mul195_out1 = concat201_out1.mul(cast245_out1);
        let add152_out1 = mul194_out1.add(mul195_out1);
        let cast248_out1 = reshape150_out1;
        let cast249_out1 = reshape151_out1;
        let mul196_out1 = squeeze71_out1.clone().mul(cast248_out1);
        let shape186_out1: [i64; 4] = {
            let axes = &squeeze71_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather155_out1 = shape186_out1[3] as i64;
        let constant2277_out1 = 2i64;
        let div96_out1 = gather155_out1 / constant2277_out1;
        let cast250_out1 = div96_out1;
        let cast251_out1 = cast250_out1;
        let unsqueeze458_out1 = [cast251_out1 as i64];
        let slice218_out1 = squeeze71_out1
            .clone()
            .slice(s![.., .., .., unsqueeze458_out1[0]..9223372036854775807]);
        let neg48_out1 = slice218_out1.neg();
        let unsqueeze459_out1 = [cast251_out1 as i64];
        let slice219_out1 = squeeze71_out1
            .slice(s![.., .., .., 0..unsqueeze459_out1[0]]);
        let concat202_out1 = burn::tensor::Tensor::cat(
            [neg48_out1, slice219_out1].into(),
            3,
        );
        let mul197_out1 = concat202_out1.mul(cast249_out1);
        let add153_out1 = mul196_out1.add(mul197_out1);
        let shape187_out1: [i64; 4] = {
            let axes = &add152_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let slice220_out1: [i64; 1] = shape187_out1[3..4].try_into().unwrap();
        let cast252_out1 = {
            let shape_array = slice220_out1 as [i64; 1usize];
            let float_array: [f64; 1usize] = shape_array.map(|x| x as f64);
            Tensor::<
                1,
            >::from_data(
                TensorData::from(float_array),
                (&self.device, burn::tensor::DType::F32),
            )
        };
        let sqrt70_out1 = cast252_out1.sqrt();
        let constant2288_out1 = self.constant2288.val();
        let div97_out1 = constant2288_out1.div(sqrt70_out1);
        let cast253_out1 = div97_out1;
        let transpose71_out1 = add153_out1.permute([0, 1, 3, 2]);
        let sqrt71_out1 = cast253_out1.clone().sqrt();
        let mul198_out1 = add152_out1
            .mul((sqrt71_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let sqrt72_out1 = cast253_out1.sqrt();
        let mul199_out1 = transpose71_out1
            .mul((sqrt72_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let matmul141_out1 = mul198_out1.matmul(mul199_out1);
        let constant2290_out1 = self.constant2290.val();
        let constant2289_out1 = self.constant2289.val();
        let where53_out1 = constant2290_out1
            .unsqueeze_dims(&[0isize, 1isize, 2isize])
            .mask_where(
                less4_out1,
                constant2289_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let add154_out1 = matmul141_out1.add(where53_out1);
        let softmax24_out1 = burn::tensor::activation::softmax(add154_out1, 3);
        let isnan24_out1 = softmax24_out1.clone().is_nan();
        let constant2291_out1 = self.constant2291.val();
        let where54_out1 = softmax24_out1
            .mask_where(
                isnan24_out1,
                constant2291_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let matmul142_out1 = where54_out1.matmul(squeeze72_out1);
        let transpose72_out1 = matmul142_out1.permute([0, 2, 1, 3]);
        let unsqueeze460_out1 = [gather150_out1 as i64];
        let unsqueeze461_out1 = [gather151_out1 as i64];
        let constant2294_out1: [i64; 1] = [512i64];
        let concat203_out1: [i64; 3usize] = [
            &unsqueeze460_out1[..],
            &unsqueeze461_out1[..],
            &constant2294_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape154_out1 = transpose72_out1.reshape(concat203_out1);
        let linear95_out1 = self.linear95.forward(reshape154_out1);
        let add155_out1 = add151_out1.add(linear95_out1);
        add155_out1
    }
}
#[derive(Module, Debug)]
pub struct Submodule26 {
    layernormalization49: LayerNorm,
    linear96: Linear,
    constant2295: burn::module::Param<Tensor<1>>,
    constant2296: burn::module::Param<Tensor<1>>,
    constant2297: burn::module::Param<Tensor<1>>,
    linear97: Linear,
    layernormalization50: LayerNorm,
    linear98: Linear,
    constant2365: burn::module::Param<Tensor<1>>,
    constant2367: burn::module::Param<Tensor<1>>,
    constant2366: burn::module::Param<Tensor<1>>,
    constant2368: burn::module::Param<Tensor<1>>,
    linear99: Linear,
    #[module(skip)]
    device: Device,
}
impl Submodule26 {
    #[allow(unused_variables)]
    pub fn new(device: &Device) -> Self {
        let layernormalization49 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear96 = LinearConfig::new(512, 2048).with_bias(true).init(device);
        let constant2295: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1.4142135381698608f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant2296: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant2297: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([0.5f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let linear97 = LinearConfig::new(2048, 512).with_bias(true).init(device);
        let layernormalization50 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear98 = LinearConfig::new(512, 1536).with_bias(false).init(device);
        let constant2365: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant2367: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant2366: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant2368: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let linear99 = LinearConfig::new(512, 512).with_bias(true).init(device);
        Self {
            layernormalization49,
            linear96,
            constant2295,
            constant2296,
            constant2297,
            linear97,
            layernormalization50,
            linear98,
            constant2365,
            constant2367,
            constant2366,
            constant2368,
            linear99,
            device: device.clone(),
        }
    }
    #[allow(clippy::let_and_return, clippy::approx_constant)]
    pub fn forward(
        &self,
        add155_out1: Tensor<3>,
        constant461_out1: Tensor<2>,
        constant474_out1: Tensor<2>,
        less4_out1: Tensor<4, Bool>,
    ) -> Tensor<3> {
        let layernormalization49_out1 = {
            let dtype = add155_out1.clone().dtype();
            self.layernormalization49
                .forward(add155_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let linear96_out1 = self.linear96.forward(layernormalization49_out1);
        let constant2295_out1 = self.constant2295.val();
        let div98_out1 = linear96_out1
            .clone()
            .div((constant2295_out1).unsqueeze_dims(&[0isize, 1isize]));
        let erf24_out1 = div98_out1.erf();
        let constant2296_out1 = self.constant2296.val();
        let add156_out1 = erf24_out1
            .add((constant2296_out1).unsqueeze_dims(&[0isize, 1isize]));
        let mul200_out1 = linear96_out1.mul(add156_out1);
        let constant2297_out1 = self.constant2297.val();
        let mul201_out1 = mul200_out1
            .mul((constant2297_out1).unsqueeze_dims(&[0isize, 1isize]));
        let linear97_out1 = self.linear97.forward(mul201_out1);
        let add157_out1 = add155_out1.add(linear97_out1);
        let layernormalization50_out1 = {
            let dtype = add157_out1.clone().dtype();
            self.layernormalization50
                .forward(add157_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let shape188_out1: [i64; 3] = {
            let axes = &layernormalization50_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather156_out1 = shape188_out1[0] as i64;
        let shape189_out1: [i64; 3] = {
            let axes = &layernormalization50_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather157_out1 = shape189_out1[1] as i64;
        let linear98_out1 = self.linear98.forward(layernormalization50_out1);
        let unsqueeze462_out1 = [gather156_out1 as i64];
        let unsqueeze463_out1 = [gather157_out1 as i64];
        let constant2304_out1: [i64; 1] = [64i64];
        let constant2302_out1: [i64; 1] = [3i64];
        let constant2303_out1: [i64; 1] = [8i64];
        let concat204_out1: [i64; 5usize] = [
            &unsqueeze462_out1[..],
            &unsqueeze463_out1[..],
            &constant2302_out1[..],
            &constant2303_out1[..],
            &constant2304_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape155_out1 = linear98_out1.reshape(concat204_out1);
        let transpose73_out1 = reshape155_out1.permute([2, 0, 3, 1, 4]);
        let split_tensors = transpose73_out1.split_with_sizes([1, 1, 1].into(), 0);
        let [split25_out1, split25_out2, split25_out3] = split_tensors
            .try_into()
            .unwrap();
        let squeeze73_out1 = split25_out1.squeeze_dims::<4>(&[0]);
        let squeeze74_out1 = split25_out2.squeeze_dims::<4>(&[0]);
        let squeeze75_out1 = split25_out3.squeeze_dims::<4>(&[0]);
        let shape190_out1: [i64; 4] = {
            let axes = &squeeze73_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather158_out1 = shape190_out1[2] as i64;
        let shape191_out1: [i64; 4] = {
            let axes = &squeeze74_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather159_out1 = shape191_out1[2] as i64;
        let sub27_out1 = gather159_out1 - gather158_out1;
        let unsqueeze464_out1 = [gather159_out1 as i64];
        let slice221_out1 = constant461_out1
            .clone()
            .slice(s![0..unsqueeze464_out1[0], ..]);
        let unsqueeze465_out1 = [gather159_out1 as i64];
        let constant2318_out1: [i64; 1] = [64i64];
        let constant2315_out1: [i64; 1] = [1i64];
        let constant2316_out1: [i64; 1] = [1i64];
        let concat205_out1: [i64; 4usize] = [
            &constant2315_out1[..],
            &constant2316_out1[..],
            &unsqueeze465_out1[..],
            &constant2318_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze466_out1 = [gather159_out1 as i64];
        let constant2322_out1: [i64; 1] = [64i64];
        let constant2319_out1: [i64; 1] = [1i64];
        let constant2320_out1: [i64; 1] = [1i64];
        let concat206_out1: [i64; 4usize] = [
            &constant2319_out1[..],
            &constant2320_out1[..],
            &unsqueeze466_out1[..],
            &constant2322_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape156_out1 = slice221_out1.reshape(concat205_out1);
        let unsqueeze467_out1 = [gather159_out1 as i64];
        let slice222_out1 = constant474_out1
            .clone()
            .slice(s![0..unsqueeze467_out1[0], ..]);
        let reshape157_out1 = slice222_out1.reshape(concat206_out1);
        let unsqueeze468_out1 = [sub27_out1 as i64];
        let unsqueeze469_out1 = [gather159_out1 as i64];
        let slice223_out1 = constant461_out1
            .slice(s![unsqueeze468_out1[0]..unsqueeze469_out1[0], ..]);
        let unsqueeze470_out1 = [gather158_out1 as i64];
        let constant2334_out1: [i64; 1] = [64i64];
        let constant2331_out1: [i64; 1] = [1i64];
        let constant2332_out1: [i64; 1] = [1i64];
        let concat207_out1: [i64; 4usize] = [
            &constant2331_out1[..],
            &constant2332_out1[..],
            &unsqueeze470_out1[..],
            &constant2334_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze471_out1 = [gather158_out1 as i64];
        let constant2338_out1: [i64; 1] = [64i64];
        let constant2335_out1: [i64; 1] = [1i64];
        let constant2336_out1: [i64; 1] = [1i64];
        let concat208_out1: [i64; 4usize] = [
            &constant2335_out1[..],
            &constant2336_out1[..],
            &unsqueeze471_out1[..],
            &constant2338_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape158_out1 = slice223_out1.reshape(concat207_out1);
        let unsqueeze472_out1 = [sub27_out1 as i64];
        let unsqueeze473_out1 = [gather159_out1 as i64];
        let slice224_out1 = constant474_out1
            .slice(s![unsqueeze472_out1[0]..unsqueeze473_out1[0], ..]);
        let reshape159_out1 = slice224_out1.reshape(concat208_out1);
        let cast254_out1 = reshape158_out1;
        let cast255_out1 = reshape159_out1;
        let mul202_out1 = squeeze73_out1.clone().mul(cast254_out1);
        let shape192_out1: [i64; 4] = {
            let axes = &squeeze73_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather160_out1 = shape192_out1[3] as i64;
        let constant2344_out1 = 2i64;
        let div99_out1 = gather160_out1 / constant2344_out1;
        let cast256_out1 = div99_out1;
        let cast257_out1 = cast256_out1;
        let unsqueeze474_out1 = [cast257_out1 as i64];
        let slice225_out1 = squeeze73_out1
            .clone()
            .slice(s![.., .., .., unsqueeze474_out1[0]..9223372036854775807]);
        let neg49_out1 = slice225_out1.neg();
        let unsqueeze475_out1 = [cast257_out1 as i64];
        let slice226_out1 = squeeze73_out1
            .slice(s![.., .., .., 0..unsqueeze475_out1[0]]);
        let concat209_out1 = burn::tensor::Tensor::cat(
            [neg49_out1, slice226_out1].into(),
            3,
        );
        let mul203_out1 = concat209_out1.mul(cast255_out1);
        let add158_out1 = mul202_out1.add(mul203_out1);
        let cast258_out1 = reshape156_out1;
        let cast259_out1 = reshape157_out1;
        let mul204_out1 = squeeze74_out1.clone().mul(cast258_out1);
        let shape193_out1: [i64; 4] = {
            let axes = &squeeze74_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather161_out1 = shape193_out1[3] as i64;
        let constant2354_out1 = 2i64;
        let div100_out1 = gather161_out1 / constant2354_out1;
        let cast260_out1 = div100_out1;
        let cast261_out1 = cast260_out1;
        let unsqueeze476_out1 = [cast261_out1 as i64];
        let slice227_out1 = squeeze74_out1
            .clone()
            .slice(s![.., .., .., unsqueeze476_out1[0]..9223372036854775807]);
        let neg50_out1 = slice227_out1.neg();
        let unsqueeze477_out1 = [cast261_out1 as i64];
        let slice228_out1 = squeeze74_out1
            .slice(s![.., .., .., 0..unsqueeze477_out1[0]]);
        let concat210_out1 = burn::tensor::Tensor::cat(
            [neg50_out1, slice228_out1].into(),
            3,
        );
        let mul205_out1 = concat210_out1.mul(cast259_out1);
        let add159_out1 = mul204_out1.add(mul205_out1);
        let shape194_out1: [i64; 4] = {
            let axes = &add158_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let slice229_out1: [i64; 1] = shape194_out1[3..4].try_into().unwrap();
        let cast262_out1 = {
            let shape_array = slice229_out1 as [i64; 1usize];
            let float_array: [f64; 1usize] = shape_array.map(|x| x as f64);
            Tensor::<
                1,
            >::from_data(
                TensorData::from(float_array),
                (&self.device, burn::tensor::DType::F32),
            )
        };
        let sqrt73_out1 = cast262_out1.sqrt();
        let constant2365_out1 = self.constant2365.val();
        let div101_out1 = constant2365_out1.div(sqrt73_out1);
        let cast263_out1 = div101_out1;
        let transpose74_out1 = add159_out1.permute([0, 1, 3, 2]);
        let sqrt74_out1 = cast263_out1.clone().sqrt();
        let mul206_out1 = add158_out1
            .mul((sqrt74_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let sqrt75_out1 = cast263_out1.sqrt();
        let mul207_out1 = transpose74_out1
            .mul((sqrt75_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let matmul147_out1 = mul206_out1.matmul(mul207_out1);
        let constant2367_out1 = self.constant2367.val();
        let constant2366_out1 = self.constant2366.val();
        let where55_out1 = constant2367_out1
            .unsqueeze_dims(&[0isize, 1isize, 2isize])
            .mask_where(
                less4_out1,
                constant2366_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let add160_out1 = matmul147_out1.add(where55_out1);
        let softmax25_out1 = burn::tensor::activation::softmax(add160_out1, 3);
        let isnan25_out1 = softmax25_out1.clone().is_nan();
        let constant2368_out1 = self.constant2368.val();
        let where56_out1 = softmax25_out1
            .mask_where(
                isnan25_out1,
                constant2368_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let matmul148_out1 = where56_out1.matmul(squeeze75_out1);
        let transpose75_out1 = matmul148_out1.permute([0, 2, 1, 3]);
        let unsqueeze478_out1 = [gather156_out1 as i64];
        let unsqueeze479_out1 = [gather157_out1 as i64];
        let constant2371_out1: [i64; 1] = [512i64];
        let concat211_out1: [i64; 3usize] = [
            &unsqueeze478_out1[..],
            &unsqueeze479_out1[..],
            &constant2371_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape160_out1 = transpose75_out1.reshape(concat211_out1);
        let linear99_out1 = self.linear99.forward(reshape160_out1);
        let add161_out1 = add157_out1.add(linear99_out1);
        add161_out1
    }
}
#[derive(Module, Debug)]
pub struct Submodule27 {
    layernormalization51: LayerNorm,
    linear100: Linear,
    constant2372: burn::module::Param<Tensor<1>>,
    constant2373: burn::module::Param<Tensor<1>>,
    constant2374: burn::module::Param<Tensor<1>>,
    linear101: Linear,
    layernormalization52: LayerNorm,
    linear102: Linear,
    constant2442: burn::module::Param<Tensor<1>>,
    constant2444: burn::module::Param<Tensor<1>>,
    constant2443: burn::module::Param<Tensor<1>>,
    constant2445: burn::module::Param<Tensor<1>>,
    linear103: Linear,
    #[module(skip)]
    device: Device,
}
impl Submodule27 {
    #[allow(unused_variables)]
    pub fn new(device: &Device) -> Self {
        let layernormalization51 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear100 = LinearConfig::new(512, 2048).with_bias(true).init(device);
        let constant2372: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1.4142135381698608f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant2373: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant2374: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([0.5f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let linear101 = LinearConfig::new(2048, 512).with_bias(true).init(device);
        let layernormalization52 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear102 = LinearConfig::new(512, 1536).with_bias(false).init(device);
        let constant2442: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant2444: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant2443: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant2445: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let linear103 = LinearConfig::new(512, 512).with_bias(true).init(device);
        Self {
            layernormalization51,
            linear100,
            constant2372,
            constant2373,
            constant2374,
            linear101,
            layernormalization52,
            linear102,
            constant2442,
            constant2444,
            constant2443,
            constant2445,
            linear103,
            device: device.clone(),
        }
    }
    #[allow(clippy::let_and_return, clippy::approx_constant)]
    pub fn forward(
        &self,
        add161_out1: Tensor<3>,
        constant461_out1: Tensor<2>,
        constant474_out1: Tensor<2>,
        less4_out1: Tensor<4, Bool>,
    ) -> Tensor<3> {
        let layernormalization51_out1 = {
            let dtype = add161_out1.clone().dtype();
            self.layernormalization51
                .forward(add161_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let linear100_out1 = self.linear100.forward(layernormalization51_out1);
        let constant2372_out1 = self.constant2372.val();
        let div102_out1 = linear100_out1
            .clone()
            .div((constant2372_out1).unsqueeze_dims(&[0isize, 1isize]));
        let erf25_out1 = div102_out1.erf();
        let constant2373_out1 = self.constant2373.val();
        let add162_out1 = erf25_out1
            .add((constant2373_out1).unsqueeze_dims(&[0isize, 1isize]));
        let mul208_out1 = linear100_out1.mul(add162_out1);
        let constant2374_out1 = self.constant2374.val();
        let mul209_out1 = mul208_out1
            .mul((constant2374_out1).unsqueeze_dims(&[0isize, 1isize]));
        let linear101_out1 = self.linear101.forward(mul209_out1);
        let add163_out1 = add161_out1.add(linear101_out1);
        let layernormalization52_out1 = {
            let dtype = add163_out1.clone().dtype();
            self.layernormalization52
                .forward(add163_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let shape195_out1: [i64; 3] = {
            let axes = &layernormalization52_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather162_out1 = shape195_out1[0] as i64;
        let shape196_out1: [i64; 3] = {
            let axes = &layernormalization52_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather163_out1 = shape196_out1[1] as i64;
        let linear102_out1 = self.linear102.forward(layernormalization52_out1);
        let unsqueeze480_out1 = [gather162_out1 as i64];
        let unsqueeze481_out1 = [gather163_out1 as i64];
        let constant2381_out1: [i64; 1] = [64i64];
        let constant2379_out1: [i64; 1] = [3i64];
        let constant2380_out1: [i64; 1] = [8i64];
        let concat212_out1: [i64; 5usize] = [
            &unsqueeze480_out1[..],
            &unsqueeze481_out1[..],
            &constant2379_out1[..],
            &constant2380_out1[..],
            &constant2381_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape161_out1 = linear102_out1.reshape(concat212_out1);
        let transpose76_out1 = reshape161_out1.permute([2, 0, 3, 1, 4]);
        let split_tensors = transpose76_out1.split_with_sizes([1, 1, 1].into(), 0);
        let [split26_out1, split26_out2, split26_out3] = split_tensors
            .try_into()
            .unwrap();
        let squeeze76_out1 = split26_out1.squeeze_dims::<4>(&[0]);
        let squeeze77_out1 = split26_out2.squeeze_dims::<4>(&[0]);
        let squeeze78_out1 = split26_out3.squeeze_dims::<4>(&[0]);
        let shape197_out1: [i64; 4] = {
            let axes = &squeeze76_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather164_out1 = shape197_out1[2] as i64;
        let shape198_out1: [i64; 4] = {
            let axes = &squeeze77_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather165_out1 = shape198_out1[2] as i64;
        let sub28_out1 = gather165_out1 - gather164_out1;
        let unsqueeze482_out1 = [gather165_out1 as i64];
        let slice230_out1 = constant461_out1
            .clone()
            .slice(s![0..unsqueeze482_out1[0], ..]);
        let unsqueeze483_out1 = [gather165_out1 as i64];
        let constant2395_out1: [i64; 1] = [64i64];
        let constant2392_out1: [i64; 1] = [1i64];
        let constant2393_out1: [i64; 1] = [1i64];
        let concat213_out1: [i64; 4usize] = [
            &constant2392_out1[..],
            &constant2393_out1[..],
            &unsqueeze483_out1[..],
            &constant2395_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze484_out1 = [gather165_out1 as i64];
        let constant2399_out1: [i64; 1] = [64i64];
        let constant2396_out1: [i64; 1] = [1i64];
        let constant2397_out1: [i64; 1] = [1i64];
        let concat214_out1: [i64; 4usize] = [
            &constant2396_out1[..],
            &constant2397_out1[..],
            &unsqueeze484_out1[..],
            &constant2399_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape162_out1 = slice230_out1.reshape(concat213_out1);
        let unsqueeze485_out1 = [gather165_out1 as i64];
        let slice231_out1 = constant474_out1
            .clone()
            .slice(s![0..unsqueeze485_out1[0], ..]);
        let reshape163_out1 = slice231_out1.reshape(concat214_out1);
        let unsqueeze486_out1 = [sub28_out1 as i64];
        let unsqueeze487_out1 = [gather165_out1 as i64];
        let slice232_out1 = constant461_out1
            .slice(s![unsqueeze486_out1[0]..unsqueeze487_out1[0], ..]);
        let unsqueeze488_out1 = [gather164_out1 as i64];
        let constant2411_out1: [i64; 1] = [64i64];
        let constant2408_out1: [i64; 1] = [1i64];
        let constant2409_out1: [i64; 1] = [1i64];
        let concat215_out1: [i64; 4usize] = [
            &constant2408_out1[..],
            &constant2409_out1[..],
            &unsqueeze488_out1[..],
            &constant2411_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze489_out1 = [gather164_out1 as i64];
        let constant2415_out1: [i64; 1] = [64i64];
        let constant2412_out1: [i64; 1] = [1i64];
        let constant2413_out1: [i64; 1] = [1i64];
        let concat216_out1: [i64; 4usize] = [
            &constant2412_out1[..],
            &constant2413_out1[..],
            &unsqueeze489_out1[..],
            &constant2415_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape164_out1 = slice232_out1.reshape(concat215_out1);
        let unsqueeze490_out1 = [sub28_out1 as i64];
        let unsqueeze491_out1 = [gather165_out1 as i64];
        let slice233_out1 = constant474_out1
            .slice(s![unsqueeze490_out1[0]..unsqueeze491_out1[0], ..]);
        let reshape165_out1 = slice233_out1.reshape(concat216_out1);
        let cast264_out1 = reshape164_out1;
        let cast265_out1 = reshape165_out1;
        let mul210_out1 = squeeze76_out1.clone().mul(cast264_out1);
        let shape199_out1: [i64; 4] = {
            let axes = &squeeze76_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather166_out1 = shape199_out1[3] as i64;
        let constant2421_out1 = 2i64;
        let div103_out1 = gather166_out1 / constant2421_out1;
        let cast266_out1 = div103_out1;
        let cast267_out1 = cast266_out1;
        let unsqueeze492_out1 = [cast267_out1 as i64];
        let slice234_out1 = squeeze76_out1
            .clone()
            .slice(s![.., .., .., unsqueeze492_out1[0]..9223372036854775807]);
        let neg51_out1 = slice234_out1.neg();
        let unsqueeze493_out1 = [cast267_out1 as i64];
        let slice235_out1 = squeeze76_out1
            .slice(s![.., .., .., 0..unsqueeze493_out1[0]]);
        let concat217_out1 = burn::tensor::Tensor::cat(
            [neg51_out1, slice235_out1].into(),
            3,
        );
        let mul211_out1 = concat217_out1.mul(cast265_out1);
        let add164_out1 = mul210_out1.add(mul211_out1);
        let cast268_out1 = reshape162_out1;
        let cast269_out1 = reshape163_out1;
        let mul212_out1 = squeeze77_out1.clone().mul(cast268_out1);
        let shape200_out1: [i64; 4] = {
            let axes = &squeeze77_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather167_out1 = shape200_out1[3] as i64;
        let constant2431_out1 = 2i64;
        let div104_out1 = gather167_out1 / constant2431_out1;
        let cast270_out1 = div104_out1;
        let cast271_out1 = cast270_out1;
        let unsqueeze494_out1 = [cast271_out1 as i64];
        let slice236_out1 = squeeze77_out1
            .clone()
            .slice(s![.., .., .., unsqueeze494_out1[0]..9223372036854775807]);
        let neg52_out1 = slice236_out1.neg();
        let unsqueeze495_out1 = [cast271_out1 as i64];
        let slice237_out1 = squeeze77_out1
            .slice(s![.., .., .., 0..unsqueeze495_out1[0]]);
        let concat218_out1 = burn::tensor::Tensor::cat(
            [neg52_out1, slice237_out1].into(),
            3,
        );
        let mul213_out1 = concat218_out1.mul(cast269_out1);
        let add165_out1 = mul212_out1.add(mul213_out1);
        let shape201_out1: [i64; 4] = {
            let axes = &add164_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let slice238_out1: [i64; 1] = shape201_out1[3..4].try_into().unwrap();
        let cast272_out1 = {
            let shape_array = slice238_out1 as [i64; 1usize];
            let float_array: [f64; 1usize] = shape_array.map(|x| x as f64);
            Tensor::<
                1,
            >::from_data(
                TensorData::from(float_array),
                (&self.device, burn::tensor::DType::F32),
            )
        };
        let sqrt76_out1 = cast272_out1.sqrt();
        let constant2442_out1 = self.constant2442.val();
        let div105_out1 = constant2442_out1.div(sqrt76_out1);
        let cast273_out1 = div105_out1;
        let transpose77_out1 = add165_out1.permute([0, 1, 3, 2]);
        let sqrt77_out1 = cast273_out1.clone().sqrt();
        let mul214_out1 = add164_out1
            .mul((sqrt77_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let sqrt78_out1 = cast273_out1.sqrt();
        let mul215_out1 = transpose77_out1
            .mul((sqrt78_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let matmul153_out1 = mul214_out1.matmul(mul215_out1);
        let constant2444_out1 = self.constant2444.val();
        let constant2443_out1 = self.constant2443.val();
        let where57_out1 = constant2444_out1
            .unsqueeze_dims(&[0isize, 1isize, 2isize])
            .mask_where(
                less4_out1,
                constant2443_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let add166_out1 = matmul153_out1.add(where57_out1);
        let softmax26_out1 = burn::tensor::activation::softmax(add166_out1, 3);
        let isnan26_out1 = softmax26_out1.clone().is_nan();
        let constant2445_out1 = self.constant2445.val();
        let where58_out1 = softmax26_out1
            .mask_where(
                isnan26_out1,
                constant2445_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let matmul154_out1 = where58_out1.matmul(squeeze78_out1);
        let transpose78_out1 = matmul154_out1.permute([0, 2, 1, 3]);
        let unsqueeze496_out1 = [gather162_out1 as i64];
        let unsqueeze497_out1 = [gather163_out1 as i64];
        let constant2448_out1: [i64; 1] = [512i64];
        let concat219_out1: [i64; 3usize] = [
            &unsqueeze496_out1[..],
            &unsqueeze497_out1[..],
            &constant2448_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape166_out1 = transpose78_out1.reshape(concat219_out1);
        let linear103_out1 = self.linear103.forward(reshape166_out1);
        let add167_out1 = add163_out1.add(linear103_out1);
        add167_out1
    }
}
#[derive(Module, Debug)]
pub struct Submodule28 {
    layernormalization53: LayerNorm,
    linear104: Linear,
    constant2449: burn::module::Param<Tensor<1>>,
    constant2450: burn::module::Param<Tensor<1>>,
    constant2451: burn::module::Param<Tensor<1>>,
    linear105: Linear,
    layernormalization54: LayerNorm,
    linear106: Linear,
    constant2519: burn::module::Param<Tensor<1>>,
    constant2521: burn::module::Param<Tensor<1>>,
    constant2520: burn::module::Param<Tensor<1>>,
    constant2522: burn::module::Param<Tensor<1>>,
    linear107: Linear,
    #[module(skip)]
    device: Device,
}
impl Submodule28 {
    #[allow(unused_variables)]
    pub fn new(device: &Device) -> Self {
        let layernormalization53 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear104 = LinearConfig::new(512, 2048).with_bias(true).init(device);
        let constant2449: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1.4142135381698608f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant2450: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant2451: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([0.5f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let linear105 = LinearConfig::new(2048, 512).with_bias(true).init(device);
        let layernormalization54 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear106 = LinearConfig::new(512, 1536).with_bias(false).init(device);
        let constant2519: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant2521: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant2520: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant2522: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let linear107 = LinearConfig::new(512, 512).with_bias(true).init(device);
        Self {
            layernormalization53,
            linear104,
            constant2449,
            constant2450,
            constant2451,
            linear105,
            layernormalization54,
            linear106,
            constant2519,
            constant2521,
            constant2520,
            constant2522,
            linear107,
            device: device.clone(),
        }
    }
    #[allow(clippy::let_and_return, clippy::approx_constant)]
    pub fn forward(
        &self,
        add167_out1: Tensor<3>,
        constant461_out1: Tensor<2>,
        constant474_out1: Tensor<2>,
        less4_out1: Tensor<4, Bool>,
    ) -> Tensor<3> {
        let layernormalization53_out1 = {
            let dtype = add167_out1.clone().dtype();
            self.layernormalization53
                .forward(add167_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let linear104_out1 = self.linear104.forward(layernormalization53_out1);
        let constant2449_out1 = self.constant2449.val();
        let div106_out1 = linear104_out1
            .clone()
            .div((constant2449_out1).unsqueeze_dims(&[0isize, 1isize]));
        let erf26_out1 = div106_out1.erf();
        let constant2450_out1 = self.constant2450.val();
        let add168_out1 = erf26_out1
            .add((constant2450_out1).unsqueeze_dims(&[0isize, 1isize]));
        let mul216_out1 = linear104_out1.mul(add168_out1);
        let constant2451_out1 = self.constant2451.val();
        let mul217_out1 = mul216_out1
            .mul((constant2451_out1).unsqueeze_dims(&[0isize, 1isize]));
        let linear105_out1 = self.linear105.forward(mul217_out1);
        let add169_out1 = add167_out1.add(linear105_out1);
        let layernormalization54_out1 = {
            let dtype = add169_out1.clone().dtype();
            self.layernormalization54
                .forward(add169_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let shape202_out1: [i64; 3] = {
            let axes = &layernormalization54_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather168_out1 = shape202_out1[0] as i64;
        let shape203_out1: [i64; 3] = {
            let axes = &layernormalization54_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather169_out1 = shape203_out1[1] as i64;
        let linear106_out1 = self.linear106.forward(layernormalization54_out1);
        let unsqueeze498_out1 = [gather168_out1 as i64];
        let unsqueeze499_out1 = [gather169_out1 as i64];
        let constant2458_out1: [i64; 1] = [64i64];
        let constant2456_out1: [i64; 1] = [3i64];
        let constant2457_out1: [i64; 1] = [8i64];
        let concat220_out1: [i64; 5usize] = [
            &unsqueeze498_out1[..],
            &unsqueeze499_out1[..],
            &constant2456_out1[..],
            &constant2457_out1[..],
            &constant2458_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape167_out1 = linear106_out1.reshape(concat220_out1);
        let transpose79_out1 = reshape167_out1.permute([2, 0, 3, 1, 4]);
        let split_tensors = transpose79_out1.split_with_sizes([1, 1, 1].into(), 0);
        let [split27_out1, split27_out2, split27_out3] = split_tensors
            .try_into()
            .unwrap();
        let squeeze79_out1 = split27_out1.squeeze_dims::<4>(&[0]);
        let squeeze80_out1 = split27_out2.squeeze_dims::<4>(&[0]);
        let squeeze81_out1 = split27_out3.squeeze_dims::<4>(&[0]);
        let shape204_out1: [i64; 4] = {
            let axes = &squeeze79_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather170_out1 = shape204_out1[2] as i64;
        let shape205_out1: [i64; 4] = {
            let axes = &squeeze80_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather171_out1 = shape205_out1[2] as i64;
        let sub29_out1 = gather171_out1 - gather170_out1;
        let unsqueeze500_out1 = [gather171_out1 as i64];
        let slice239_out1 = constant461_out1
            .clone()
            .slice(s![0..unsqueeze500_out1[0], ..]);
        let unsqueeze501_out1 = [gather171_out1 as i64];
        let constant2472_out1: [i64; 1] = [64i64];
        let constant2469_out1: [i64; 1] = [1i64];
        let constant2470_out1: [i64; 1] = [1i64];
        let concat221_out1: [i64; 4usize] = [
            &constant2469_out1[..],
            &constant2470_out1[..],
            &unsqueeze501_out1[..],
            &constant2472_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze502_out1 = [gather171_out1 as i64];
        let constant2476_out1: [i64; 1] = [64i64];
        let constant2473_out1: [i64; 1] = [1i64];
        let constant2474_out1: [i64; 1] = [1i64];
        let concat222_out1: [i64; 4usize] = [
            &constant2473_out1[..],
            &constant2474_out1[..],
            &unsqueeze502_out1[..],
            &constant2476_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape168_out1 = slice239_out1.reshape(concat221_out1);
        let unsqueeze503_out1 = [gather171_out1 as i64];
        let slice240_out1 = constant474_out1
            .clone()
            .slice(s![0..unsqueeze503_out1[0], ..]);
        let reshape169_out1 = slice240_out1.reshape(concat222_out1);
        let unsqueeze504_out1 = [sub29_out1 as i64];
        let unsqueeze505_out1 = [gather171_out1 as i64];
        let slice241_out1 = constant461_out1
            .slice(s![unsqueeze504_out1[0]..unsqueeze505_out1[0], ..]);
        let unsqueeze506_out1 = [gather170_out1 as i64];
        let constant2488_out1: [i64; 1] = [64i64];
        let constant2485_out1: [i64; 1] = [1i64];
        let constant2486_out1: [i64; 1] = [1i64];
        let concat223_out1: [i64; 4usize] = [
            &constant2485_out1[..],
            &constant2486_out1[..],
            &unsqueeze506_out1[..],
            &constant2488_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze507_out1 = [gather170_out1 as i64];
        let constant2492_out1: [i64; 1] = [64i64];
        let constant2489_out1: [i64; 1] = [1i64];
        let constant2490_out1: [i64; 1] = [1i64];
        let concat224_out1: [i64; 4usize] = [
            &constant2489_out1[..],
            &constant2490_out1[..],
            &unsqueeze507_out1[..],
            &constant2492_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape170_out1 = slice241_out1.reshape(concat223_out1);
        let unsqueeze508_out1 = [sub29_out1 as i64];
        let unsqueeze509_out1 = [gather171_out1 as i64];
        let slice242_out1 = constant474_out1
            .slice(s![unsqueeze508_out1[0]..unsqueeze509_out1[0], ..]);
        let reshape171_out1 = slice242_out1.reshape(concat224_out1);
        let cast274_out1 = reshape170_out1;
        let cast275_out1 = reshape171_out1;
        let mul218_out1 = squeeze79_out1.clone().mul(cast274_out1);
        let shape206_out1: [i64; 4] = {
            let axes = &squeeze79_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather172_out1 = shape206_out1[3] as i64;
        let constant2498_out1 = 2i64;
        let div107_out1 = gather172_out1 / constant2498_out1;
        let cast276_out1 = div107_out1;
        let cast277_out1 = cast276_out1;
        let unsqueeze510_out1 = [cast277_out1 as i64];
        let slice243_out1 = squeeze79_out1
            .clone()
            .slice(s![.., .., .., unsqueeze510_out1[0]..9223372036854775807]);
        let neg53_out1 = slice243_out1.neg();
        let unsqueeze511_out1 = [cast277_out1 as i64];
        let slice244_out1 = squeeze79_out1
            .slice(s![.., .., .., 0..unsqueeze511_out1[0]]);
        let concat225_out1 = burn::tensor::Tensor::cat(
            [neg53_out1, slice244_out1].into(),
            3,
        );
        let mul219_out1 = concat225_out1.mul(cast275_out1);
        let add170_out1 = mul218_out1.add(mul219_out1);
        let cast278_out1 = reshape168_out1;
        let cast279_out1 = reshape169_out1;
        let mul220_out1 = squeeze80_out1.clone().mul(cast278_out1);
        let shape207_out1: [i64; 4] = {
            let axes = &squeeze80_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather173_out1 = shape207_out1[3] as i64;
        let constant2508_out1 = 2i64;
        let div108_out1 = gather173_out1 / constant2508_out1;
        let cast280_out1 = div108_out1;
        let cast281_out1 = cast280_out1;
        let unsqueeze512_out1 = [cast281_out1 as i64];
        let slice245_out1 = squeeze80_out1
            .clone()
            .slice(s![.., .., .., unsqueeze512_out1[0]..9223372036854775807]);
        let neg54_out1 = slice245_out1.neg();
        let unsqueeze513_out1 = [cast281_out1 as i64];
        let slice246_out1 = squeeze80_out1
            .slice(s![.., .., .., 0..unsqueeze513_out1[0]]);
        let concat226_out1 = burn::tensor::Tensor::cat(
            [neg54_out1, slice246_out1].into(),
            3,
        );
        let mul221_out1 = concat226_out1.mul(cast279_out1);
        let add171_out1 = mul220_out1.add(mul221_out1);
        let shape208_out1: [i64; 4] = {
            let axes = &add170_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let slice247_out1: [i64; 1] = shape208_out1[3..4].try_into().unwrap();
        let cast282_out1 = {
            let shape_array = slice247_out1 as [i64; 1usize];
            let float_array: [f64; 1usize] = shape_array.map(|x| x as f64);
            Tensor::<
                1,
            >::from_data(
                TensorData::from(float_array),
                (&self.device, burn::tensor::DType::F32),
            )
        };
        let sqrt79_out1 = cast282_out1.sqrt();
        let constant2519_out1 = self.constant2519.val();
        let div109_out1 = constant2519_out1.div(sqrt79_out1);
        let cast283_out1 = div109_out1;
        let transpose80_out1 = add171_out1.permute([0, 1, 3, 2]);
        let sqrt80_out1 = cast283_out1.clone().sqrt();
        let mul222_out1 = add170_out1
            .mul((sqrt80_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let sqrt81_out1 = cast283_out1.sqrt();
        let mul223_out1 = transpose80_out1
            .mul((sqrt81_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let matmul159_out1 = mul222_out1.matmul(mul223_out1);
        let constant2521_out1 = self.constant2521.val();
        let constant2520_out1 = self.constant2520.val();
        let where59_out1 = constant2521_out1
            .unsqueeze_dims(&[0isize, 1isize, 2isize])
            .mask_where(
                less4_out1,
                constant2520_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let add172_out1 = matmul159_out1.add(where59_out1);
        let softmax27_out1 = burn::tensor::activation::softmax(add172_out1, 3);
        let isnan27_out1 = softmax27_out1.clone().is_nan();
        let constant2522_out1 = self.constant2522.val();
        let where60_out1 = softmax27_out1
            .mask_where(
                isnan27_out1,
                constant2522_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let matmul160_out1 = where60_out1.matmul(squeeze81_out1);
        let transpose81_out1 = matmul160_out1.permute([0, 2, 1, 3]);
        let unsqueeze514_out1 = [gather168_out1 as i64];
        let unsqueeze515_out1 = [gather169_out1 as i64];
        let constant2525_out1: [i64; 1] = [512i64];
        let concat227_out1: [i64; 3usize] = [
            &unsqueeze514_out1[..],
            &unsqueeze515_out1[..],
            &constant2525_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape172_out1 = transpose81_out1.reshape(concat227_out1);
        let linear107_out1 = self.linear107.forward(reshape172_out1);
        let add173_out1 = add169_out1.add(linear107_out1);
        add173_out1
    }
}
#[derive(Module, Debug)]
pub struct Submodule29 {
    layernormalization55: LayerNorm,
    linear108: Linear,
    constant2526: burn::module::Param<Tensor<1>>,
    constant2527: burn::module::Param<Tensor<1>>,
    constant2528: burn::module::Param<Tensor<1>>,
    linear109: Linear,
    layernormalization56: LayerNorm,
    linear110: Linear,
    constant2596: burn::module::Param<Tensor<1>>,
    constant2598: burn::module::Param<Tensor<1>>,
    constant2597: burn::module::Param<Tensor<1>>,
    constant2599: burn::module::Param<Tensor<1>>,
    linear111: Linear,
    #[module(skip)]
    device: Device,
}
impl Submodule29 {
    #[allow(unused_variables)]
    pub fn new(device: &Device) -> Self {
        let layernormalization55 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear108 = LinearConfig::new(512, 2048).with_bias(true).init(device);
        let constant2526: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1.4142135381698608f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant2527: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant2528: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([0.5f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let linear109 = LinearConfig::new(2048, 512).with_bias(true).init(device);
        let layernormalization56 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear110 = LinearConfig::new(512, 1536).with_bias(false).init(device);
        let constant2596: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant2598: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant2597: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant2599: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let linear111 = LinearConfig::new(512, 512).with_bias(true).init(device);
        Self {
            layernormalization55,
            linear108,
            constant2526,
            constant2527,
            constant2528,
            linear109,
            layernormalization56,
            linear110,
            constant2596,
            constant2598,
            constant2597,
            constant2599,
            linear111,
            device: device.clone(),
        }
    }
    #[allow(clippy::let_and_return, clippy::approx_constant)]
    pub fn forward(
        &self,
        add173_out1: Tensor<3>,
        constant461_out1: Tensor<2>,
        constant474_out1: Tensor<2>,
        less4_out1: Tensor<4, Bool>,
    ) -> Tensor<3> {
        let layernormalization55_out1 = {
            let dtype = add173_out1.clone().dtype();
            self.layernormalization55
                .forward(add173_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let linear108_out1 = self.linear108.forward(layernormalization55_out1);
        let constant2526_out1 = self.constant2526.val();
        let div110_out1 = linear108_out1
            .clone()
            .div((constant2526_out1).unsqueeze_dims(&[0isize, 1isize]));
        let erf27_out1 = div110_out1.erf();
        let constant2527_out1 = self.constant2527.val();
        let add174_out1 = erf27_out1
            .add((constant2527_out1).unsqueeze_dims(&[0isize, 1isize]));
        let mul224_out1 = linear108_out1.mul(add174_out1);
        let constant2528_out1 = self.constant2528.val();
        let mul225_out1 = mul224_out1
            .mul((constant2528_out1).unsqueeze_dims(&[0isize, 1isize]));
        let linear109_out1 = self.linear109.forward(mul225_out1);
        let add175_out1 = add173_out1.add(linear109_out1);
        let layernormalization56_out1 = {
            let dtype = add175_out1.clone().dtype();
            self.layernormalization56
                .forward(add175_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let shape209_out1: [i64; 3] = {
            let axes = &layernormalization56_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather174_out1 = shape209_out1[0] as i64;
        let shape210_out1: [i64; 3] = {
            let axes = &layernormalization56_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather175_out1 = shape210_out1[1] as i64;
        let linear110_out1 = self.linear110.forward(layernormalization56_out1);
        let unsqueeze516_out1 = [gather174_out1 as i64];
        let unsqueeze517_out1 = [gather175_out1 as i64];
        let constant2535_out1: [i64; 1] = [64i64];
        let constant2533_out1: [i64; 1] = [3i64];
        let constant2534_out1: [i64; 1] = [8i64];
        let concat228_out1: [i64; 5usize] = [
            &unsqueeze516_out1[..],
            &unsqueeze517_out1[..],
            &constant2533_out1[..],
            &constant2534_out1[..],
            &constant2535_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape173_out1 = linear110_out1.reshape(concat228_out1);
        let transpose82_out1 = reshape173_out1.permute([2, 0, 3, 1, 4]);
        let split_tensors = transpose82_out1.split_with_sizes([1, 1, 1].into(), 0);
        let [split28_out1, split28_out2, split28_out3] = split_tensors
            .try_into()
            .unwrap();
        let squeeze82_out1 = split28_out1.squeeze_dims::<4>(&[0]);
        let squeeze83_out1 = split28_out2.squeeze_dims::<4>(&[0]);
        let squeeze84_out1 = split28_out3.squeeze_dims::<4>(&[0]);
        let shape211_out1: [i64; 4] = {
            let axes = &squeeze82_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather176_out1 = shape211_out1[2] as i64;
        let shape212_out1: [i64; 4] = {
            let axes = &squeeze83_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather177_out1 = shape212_out1[2] as i64;
        let sub30_out1 = gather177_out1 - gather176_out1;
        let unsqueeze518_out1 = [gather177_out1 as i64];
        let slice248_out1 = constant461_out1
            .clone()
            .slice(s![0..unsqueeze518_out1[0], ..]);
        let unsqueeze519_out1 = [gather177_out1 as i64];
        let constant2549_out1: [i64; 1] = [64i64];
        let constant2546_out1: [i64; 1] = [1i64];
        let constant2547_out1: [i64; 1] = [1i64];
        let concat229_out1: [i64; 4usize] = [
            &constant2546_out1[..],
            &constant2547_out1[..],
            &unsqueeze519_out1[..],
            &constant2549_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze520_out1 = [gather177_out1 as i64];
        let constant2553_out1: [i64; 1] = [64i64];
        let constant2550_out1: [i64; 1] = [1i64];
        let constant2551_out1: [i64; 1] = [1i64];
        let concat230_out1: [i64; 4usize] = [
            &constant2550_out1[..],
            &constant2551_out1[..],
            &unsqueeze520_out1[..],
            &constant2553_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape174_out1 = slice248_out1.reshape(concat229_out1);
        let unsqueeze521_out1 = [gather177_out1 as i64];
        let slice249_out1 = constant474_out1
            .clone()
            .slice(s![0..unsqueeze521_out1[0], ..]);
        let reshape175_out1 = slice249_out1.reshape(concat230_out1);
        let unsqueeze522_out1 = [sub30_out1 as i64];
        let unsqueeze523_out1 = [gather177_out1 as i64];
        let slice250_out1 = constant461_out1
            .slice(s![unsqueeze522_out1[0]..unsqueeze523_out1[0], ..]);
        let unsqueeze524_out1 = [gather176_out1 as i64];
        let constant2565_out1: [i64; 1] = [64i64];
        let constant2562_out1: [i64; 1] = [1i64];
        let constant2563_out1: [i64; 1] = [1i64];
        let concat231_out1: [i64; 4usize] = [
            &constant2562_out1[..],
            &constant2563_out1[..],
            &unsqueeze524_out1[..],
            &constant2565_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze525_out1 = [gather176_out1 as i64];
        let constant2569_out1: [i64; 1] = [64i64];
        let constant2566_out1: [i64; 1] = [1i64];
        let constant2567_out1: [i64; 1] = [1i64];
        let concat232_out1: [i64; 4usize] = [
            &constant2566_out1[..],
            &constant2567_out1[..],
            &unsqueeze525_out1[..],
            &constant2569_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape176_out1 = slice250_out1.reshape(concat231_out1);
        let unsqueeze526_out1 = [sub30_out1 as i64];
        let unsqueeze527_out1 = [gather177_out1 as i64];
        let slice251_out1 = constant474_out1
            .slice(s![unsqueeze526_out1[0]..unsqueeze527_out1[0], ..]);
        let reshape177_out1 = slice251_out1.reshape(concat232_out1);
        let cast284_out1 = reshape176_out1;
        let cast285_out1 = reshape177_out1;
        let mul226_out1 = squeeze82_out1.clone().mul(cast284_out1);
        let shape213_out1: [i64; 4] = {
            let axes = &squeeze82_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather178_out1 = shape213_out1[3] as i64;
        let constant2575_out1 = 2i64;
        let div111_out1 = gather178_out1 / constant2575_out1;
        let cast286_out1 = div111_out1;
        let cast287_out1 = cast286_out1;
        let unsqueeze528_out1 = [cast287_out1 as i64];
        let slice252_out1 = squeeze82_out1
            .clone()
            .slice(s![.., .., .., unsqueeze528_out1[0]..9223372036854775807]);
        let neg55_out1 = slice252_out1.neg();
        let unsqueeze529_out1 = [cast287_out1 as i64];
        let slice253_out1 = squeeze82_out1
            .slice(s![.., .., .., 0..unsqueeze529_out1[0]]);
        let concat233_out1 = burn::tensor::Tensor::cat(
            [neg55_out1, slice253_out1].into(),
            3,
        );
        let mul227_out1 = concat233_out1.mul(cast285_out1);
        let add176_out1 = mul226_out1.add(mul227_out1);
        let cast288_out1 = reshape174_out1;
        let cast289_out1 = reshape175_out1;
        let mul228_out1 = squeeze83_out1.clone().mul(cast288_out1);
        let shape214_out1: [i64; 4] = {
            let axes = &squeeze83_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather179_out1 = shape214_out1[3] as i64;
        let constant2585_out1 = 2i64;
        let div112_out1 = gather179_out1 / constant2585_out1;
        let cast290_out1 = div112_out1;
        let cast291_out1 = cast290_out1;
        let unsqueeze530_out1 = [cast291_out1 as i64];
        let slice254_out1 = squeeze83_out1
            .clone()
            .slice(s![.., .., .., unsqueeze530_out1[0]..9223372036854775807]);
        let neg56_out1 = slice254_out1.neg();
        let unsqueeze531_out1 = [cast291_out1 as i64];
        let slice255_out1 = squeeze83_out1
            .slice(s![.., .., .., 0..unsqueeze531_out1[0]]);
        let concat234_out1 = burn::tensor::Tensor::cat(
            [neg56_out1, slice255_out1].into(),
            3,
        );
        let mul229_out1 = concat234_out1.mul(cast289_out1);
        let add177_out1 = mul228_out1.add(mul229_out1);
        let shape215_out1: [i64; 4] = {
            let axes = &add176_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let slice256_out1: [i64; 1] = shape215_out1[3..4].try_into().unwrap();
        let cast292_out1 = {
            let shape_array = slice256_out1 as [i64; 1usize];
            let float_array: [f64; 1usize] = shape_array.map(|x| x as f64);
            Tensor::<
                1,
            >::from_data(
                TensorData::from(float_array),
                (&self.device, burn::tensor::DType::F32),
            )
        };
        let sqrt82_out1 = cast292_out1.sqrt();
        let constant2596_out1 = self.constant2596.val();
        let div113_out1 = constant2596_out1.div(sqrt82_out1);
        let cast293_out1 = div113_out1;
        let transpose83_out1 = add177_out1.permute([0, 1, 3, 2]);
        let sqrt83_out1 = cast293_out1.clone().sqrt();
        let mul230_out1 = add176_out1
            .mul((sqrt83_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let sqrt84_out1 = cast293_out1.sqrt();
        let mul231_out1 = transpose83_out1
            .mul((sqrt84_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let matmul165_out1 = mul230_out1.matmul(mul231_out1);
        let constant2598_out1 = self.constant2598.val();
        let constant2597_out1 = self.constant2597.val();
        let where61_out1 = constant2598_out1
            .unsqueeze_dims(&[0isize, 1isize, 2isize])
            .mask_where(
                less4_out1,
                constant2597_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let add178_out1 = matmul165_out1.add(where61_out1);
        let softmax28_out1 = burn::tensor::activation::softmax(add178_out1, 3);
        let isnan28_out1 = softmax28_out1.clone().is_nan();
        let constant2599_out1 = self.constant2599.val();
        let where62_out1 = softmax28_out1
            .mask_where(
                isnan28_out1,
                constant2599_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let matmul166_out1 = where62_out1.matmul(squeeze84_out1);
        let transpose84_out1 = matmul166_out1.permute([0, 2, 1, 3]);
        let unsqueeze532_out1 = [gather174_out1 as i64];
        let unsqueeze533_out1 = [gather175_out1 as i64];
        let constant2602_out1: [i64; 1] = [512i64];
        let concat235_out1: [i64; 3usize] = [
            &unsqueeze532_out1[..],
            &unsqueeze533_out1[..],
            &constant2602_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape178_out1 = transpose84_out1.reshape(concat235_out1);
        let linear111_out1 = self.linear111.forward(reshape178_out1);
        let add179_out1 = add175_out1.add(linear111_out1);
        add179_out1
    }
}
#[derive(Module, Debug)]
pub struct Submodule30 {
    layernormalization57: LayerNorm,
    linear112: Linear,
    constant2603: burn::module::Param<Tensor<1>>,
    constant2604: burn::module::Param<Tensor<1>>,
    constant2605: burn::module::Param<Tensor<1>>,
    linear113: Linear,
    layernormalization58: LayerNorm,
    linear114: Linear,
    constant2673: burn::module::Param<Tensor<1>>,
    constant2675: burn::module::Param<Tensor<1>>,
    constant2674: burn::module::Param<Tensor<1>>,
    constant2676: burn::module::Param<Tensor<1>>,
    linear115: Linear,
    #[module(skip)]
    device: Device,
}
impl Submodule30 {
    #[allow(unused_variables)]
    pub fn new(device: &Device) -> Self {
        let layernormalization57 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear112 = LinearConfig::new(512, 2048).with_bias(true).init(device);
        let constant2603: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1.4142135381698608f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant2604: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant2605: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([0.5f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let linear113 = LinearConfig::new(2048, 512).with_bias(true).init(device);
        let layernormalization58 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear114 = LinearConfig::new(512, 1536).with_bias(false).init(device);
        let constant2673: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant2675: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant2674: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant2676: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let linear115 = LinearConfig::new(512, 512).with_bias(true).init(device);
        Self {
            layernormalization57,
            linear112,
            constant2603,
            constant2604,
            constant2605,
            linear113,
            layernormalization58,
            linear114,
            constant2673,
            constant2675,
            constant2674,
            constant2676,
            linear115,
            device: device.clone(),
        }
    }
    #[allow(clippy::let_and_return, clippy::approx_constant)]
    pub fn forward(
        &self,
        add179_out1: Tensor<3>,
        constant461_out1: Tensor<2>,
        constant474_out1: Tensor<2>,
        less4_out1: Tensor<4, Bool>,
    ) -> Tensor<3> {
        let layernormalization57_out1 = {
            let dtype = add179_out1.clone().dtype();
            self.layernormalization57
                .forward(add179_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let linear112_out1 = self.linear112.forward(layernormalization57_out1);
        let constant2603_out1 = self.constant2603.val();
        let div114_out1 = linear112_out1
            .clone()
            .div((constant2603_out1).unsqueeze_dims(&[0isize, 1isize]));
        let erf28_out1 = div114_out1.erf();
        let constant2604_out1 = self.constant2604.val();
        let add180_out1 = erf28_out1
            .add((constant2604_out1).unsqueeze_dims(&[0isize, 1isize]));
        let mul232_out1 = linear112_out1.mul(add180_out1);
        let constant2605_out1 = self.constant2605.val();
        let mul233_out1 = mul232_out1
            .mul((constant2605_out1).unsqueeze_dims(&[0isize, 1isize]));
        let linear113_out1 = self.linear113.forward(mul233_out1);
        let add181_out1 = add179_out1.add(linear113_out1);
        let layernormalization58_out1 = {
            let dtype = add181_out1.clone().dtype();
            self.layernormalization58
                .forward(add181_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let shape216_out1: [i64; 3] = {
            let axes = &layernormalization58_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather180_out1 = shape216_out1[0] as i64;
        let shape217_out1: [i64; 3] = {
            let axes = &layernormalization58_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather181_out1 = shape217_out1[1] as i64;
        let linear114_out1 = self.linear114.forward(layernormalization58_out1);
        let unsqueeze534_out1 = [gather180_out1 as i64];
        let unsqueeze535_out1 = [gather181_out1 as i64];
        let constant2612_out1: [i64; 1] = [64i64];
        let constant2610_out1: [i64; 1] = [3i64];
        let constant2611_out1: [i64; 1] = [8i64];
        let concat236_out1: [i64; 5usize] = [
            &unsqueeze534_out1[..],
            &unsqueeze535_out1[..],
            &constant2610_out1[..],
            &constant2611_out1[..],
            &constant2612_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape179_out1 = linear114_out1.reshape(concat236_out1);
        let transpose85_out1 = reshape179_out1.permute([2, 0, 3, 1, 4]);
        let split_tensors = transpose85_out1.split_with_sizes([1, 1, 1].into(), 0);
        let [split29_out1, split29_out2, split29_out3] = split_tensors
            .try_into()
            .unwrap();
        let squeeze85_out1 = split29_out1.squeeze_dims::<4>(&[0]);
        let squeeze86_out1 = split29_out2.squeeze_dims::<4>(&[0]);
        let squeeze87_out1 = split29_out3.squeeze_dims::<4>(&[0]);
        let shape218_out1: [i64; 4] = {
            let axes = &squeeze85_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather182_out1 = shape218_out1[2] as i64;
        let shape219_out1: [i64; 4] = {
            let axes = &squeeze86_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather183_out1 = shape219_out1[2] as i64;
        let sub31_out1 = gather183_out1 - gather182_out1;
        let unsqueeze536_out1 = [gather183_out1 as i64];
        let slice257_out1 = constant461_out1
            .clone()
            .slice(s![0..unsqueeze536_out1[0], ..]);
        let unsqueeze537_out1 = [gather183_out1 as i64];
        let constant2626_out1: [i64; 1] = [64i64];
        let constant2623_out1: [i64; 1] = [1i64];
        let constant2624_out1: [i64; 1] = [1i64];
        let concat237_out1: [i64; 4usize] = [
            &constant2623_out1[..],
            &constant2624_out1[..],
            &unsqueeze537_out1[..],
            &constant2626_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze538_out1 = [gather183_out1 as i64];
        let constant2630_out1: [i64; 1] = [64i64];
        let constant2627_out1: [i64; 1] = [1i64];
        let constant2628_out1: [i64; 1] = [1i64];
        let concat238_out1: [i64; 4usize] = [
            &constant2627_out1[..],
            &constant2628_out1[..],
            &unsqueeze538_out1[..],
            &constant2630_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape180_out1 = slice257_out1.reshape(concat237_out1);
        let unsqueeze539_out1 = [gather183_out1 as i64];
        let slice258_out1 = constant474_out1
            .clone()
            .slice(s![0..unsqueeze539_out1[0], ..]);
        let reshape181_out1 = slice258_out1.reshape(concat238_out1);
        let unsqueeze540_out1 = [sub31_out1 as i64];
        let unsqueeze541_out1 = [gather183_out1 as i64];
        let slice259_out1 = constant461_out1
            .slice(s![unsqueeze540_out1[0]..unsqueeze541_out1[0], ..]);
        let unsqueeze542_out1 = [gather182_out1 as i64];
        let constant2642_out1: [i64; 1] = [64i64];
        let constant2639_out1: [i64; 1] = [1i64];
        let constant2640_out1: [i64; 1] = [1i64];
        let concat239_out1: [i64; 4usize] = [
            &constant2639_out1[..],
            &constant2640_out1[..],
            &unsqueeze542_out1[..],
            &constant2642_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze543_out1 = [gather182_out1 as i64];
        let constant2646_out1: [i64; 1] = [64i64];
        let constant2643_out1: [i64; 1] = [1i64];
        let constant2644_out1: [i64; 1] = [1i64];
        let concat240_out1: [i64; 4usize] = [
            &constant2643_out1[..],
            &constant2644_out1[..],
            &unsqueeze543_out1[..],
            &constant2646_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape182_out1 = slice259_out1.reshape(concat239_out1);
        let unsqueeze544_out1 = [sub31_out1 as i64];
        let unsqueeze545_out1 = [gather183_out1 as i64];
        let slice260_out1 = constant474_out1
            .slice(s![unsqueeze544_out1[0]..unsqueeze545_out1[0], ..]);
        let reshape183_out1 = slice260_out1.reshape(concat240_out1);
        let cast294_out1 = reshape182_out1;
        let cast295_out1 = reshape183_out1;
        let mul234_out1 = squeeze85_out1.clone().mul(cast294_out1);
        let shape220_out1: [i64; 4] = {
            let axes = &squeeze85_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather184_out1 = shape220_out1[3] as i64;
        let constant2652_out1 = 2i64;
        let div115_out1 = gather184_out1 / constant2652_out1;
        let cast296_out1 = div115_out1;
        let cast297_out1 = cast296_out1;
        let unsqueeze546_out1 = [cast297_out1 as i64];
        let slice261_out1 = squeeze85_out1
            .clone()
            .slice(s![.., .., .., unsqueeze546_out1[0]..9223372036854775807]);
        let neg57_out1 = slice261_out1.neg();
        let unsqueeze547_out1 = [cast297_out1 as i64];
        let slice262_out1 = squeeze85_out1
            .slice(s![.., .., .., 0..unsqueeze547_out1[0]]);
        let concat241_out1 = burn::tensor::Tensor::cat(
            [neg57_out1, slice262_out1].into(),
            3,
        );
        let mul235_out1 = concat241_out1.mul(cast295_out1);
        let add182_out1 = mul234_out1.add(mul235_out1);
        let cast298_out1 = reshape180_out1;
        let cast299_out1 = reshape181_out1;
        let mul236_out1 = squeeze86_out1.clone().mul(cast298_out1);
        let shape221_out1: [i64; 4] = {
            let axes = &squeeze86_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather185_out1 = shape221_out1[3] as i64;
        let constant2662_out1 = 2i64;
        let div116_out1 = gather185_out1 / constant2662_out1;
        let cast300_out1 = div116_out1;
        let cast301_out1 = cast300_out1;
        let unsqueeze548_out1 = [cast301_out1 as i64];
        let slice263_out1 = squeeze86_out1
            .clone()
            .slice(s![.., .., .., unsqueeze548_out1[0]..9223372036854775807]);
        let neg58_out1 = slice263_out1.neg();
        let unsqueeze549_out1 = [cast301_out1 as i64];
        let slice264_out1 = squeeze86_out1
            .slice(s![.., .., .., 0..unsqueeze549_out1[0]]);
        let concat242_out1 = burn::tensor::Tensor::cat(
            [neg58_out1, slice264_out1].into(),
            3,
        );
        let mul237_out1 = concat242_out1.mul(cast299_out1);
        let add183_out1 = mul236_out1.add(mul237_out1);
        let shape222_out1: [i64; 4] = {
            let axes = &add182_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let slice265_out1: [i64; 1] = shape222_out1[3..4].try_into().unwrap();
        let cast302_out1 = {
            let shape_array = slice265_out1 as [i64; 1usize];
            let float_array: [f64; 1usize] = shape_array.map(|x| x as f64);
            Tensor::<
                1,
            >::from_data(
                TensorData::from(float_array),
                (&self.device, burn::tensor::DType::F32),
            )
        };
        let sqrt85_out1 = cast302_out1.sqrt();
        let constant2673_out1 = self.constant2673.val();
        let div117_out1 = constant2673_out1.div(sqrt85_out1);
        let cast303_out1 = div117_out1;
        let transpose86_out1 = add183_out1.permute([0, 1, 3, 2]);
        let sqrt86_out1 = cast303_out1.clone().sqrt();
        let mul238_out1 = add182_out1
            .mul((sqrt86_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let sqrt87_out1 = cast303_out1.sqrt();
        let mul239_out1 = transpose86_out1
            .mul((sqrt87_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let matmul171_out1 = mul238_out1.matmul(mul239_out1);
        let constant2675_out1 = self.constant2675.val();
        let constant2674_out1 = self.constant2674.val();
        let where63_out1 = constant2675_out1
            .unsqueeze_dims(&[0isize, 1isize, 2isize])
            .mask_where(
                less4_out1,
                constant2674_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let add184_out1 = matmul171_out1.add(where63_out1);
        let softmax29_out1 = burn::tensor::activation::softmax(add184_out1, 3);
        let isnan29_out1 = softmax29_out1.clone().is_nan();
        let constant2676_out1 = self.constant2676.val();
        let where64_out1 = softmax29_out1
            .mask_where(
                isnan29_out1,
                constant2676_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let matmul172_out1 = where64_out1.matmul(squeeze87_out1);
        let transpose87_out1 = matmul172_out1.permute([0, 2, 1, 3]);
        let unsqueeze550_out1 = [gather180_out1 as i64];
        let unsqueeze551_out1 = [gather181_out1 as i64];
        let constant2679_out1: [i64; 1] = [512i64];
        let concat243_out1: [i64; 3usize] = [
            &unsqueeze550_out1[..],
            &unsqueeze551_out1[..],
            &constant2679_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape184_out1 = transpose87_out1.reshape(concat243_out1);
        let linear115_out1 = self.linear115.forward(reshape184_out1);
        let add185_out1 = add181_out1.add(linear115_out1);
        add185_out1
    }
}
#[derive(Module, Debug)]
pub struct Submodule31 {
    layernormalization59: LayerNorm,
    linear116: Linear,
    constant2680: burn::module::Param<Tensor<1>>,
    constant2681: burn::module::Param<Tensor<1>>,
    constant2682: burn::module::Param<Tensor<1>>,
    linear117: Linear,
    layernormalization60: LayerNorm,
    linear118: Linear,
    constant2750: burn::module::Param<Tensor<1>>,
    constant2752: burn::module::Param<Tensor<1>>,
    constant2751: burn::module::Param<Tensor<1>>,
    constant2753: burn::module::Param<Tensor<1>>,
    linear119: Linear,
    #[module(skip)]
    device: Device,
}
impl Submodule31 {
    #[allow(unused_variables)]
    pub fn new(device: &Device) -> Self {
        let layernormalization59 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear116 = LinearConfig::new(512, 2048).with_bias(true).init(device);
        let constant2680: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1.4142135381698608f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant2681: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant2682: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([0.5f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let linear117 = LinearConfig::new(2048, 512).with_bias(true).init(device);
        let layernormalization60 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear118 = LinearConfig::new(512, 1536).with_bias(false).init(device);
        let constant2750: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant2752: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant2751: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant2753: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let linear119 = LinearConfig::new(512, 512).with_bias(true).init(device);
        Self {
            layernormalization59,
            linear116,
            constant2680,
            constant2681,
            constant2682,
            linear117,
            layernormalization60,
            linear118,
            constant2750,
            constant2752,
            constant2751,
            constant2753,
            linear119,
            device: device.clone(),
        }
    }
    #[allow(clippy::let_and_return, clippy::approx_constant)]
    pub fn forward(
        &self,
        add185_out1: Tensor<3>,
        constant461_out1: Tensor<2>,
        constant474_out1: Tensor<2>,
        less4_out1: Tensor<4, Bool>,
    ) -> Tensor<3> {
        let layernormalization59_out1 = {
            let dtype = add185_out1.clone().dtype();
            self.layernormalization59
                .forward(add185_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let linear116_out1 = self.linear116.forward(layernormalization59_out1);
        let constant2680_out1 = self.constant2680.val();
        let div118_out1 = linear116_out1
            .clone()
            .div((constant2680_out1).unsqueeze_dims(&[0isize, 1isize]));
        let erf29_out1 = div118_out1.erf();
        let constant2681_out1 = self.constant2681.val();
        let add186_out1 = erf29_out1
            .add((constant2681_out1).unsqueeze_dims(&[0isize, 1isize]));
        let mul240_out1 = linear116_out1.mul(add186_out1);
        let constant2682_out1 = self.constant2682.val();
        let mul241_out1 = mul240_out1
            .mul((constant2682_out1).unsqueeze_dims(&[0isize, 1isize]));
        let linear117_out1 = self.linear117.forward(mul241_out1);
        let add187_out1 = add185_out1.add(linear117_out1);
        let layernormalization60_out1 = {
            let dtype = add187_out1.clone().dtype();
            self.layernormalization60
                .forward(add187_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let shape223_out1: [i64; 3] = {
            let axes = &layernormalization60_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather186_out1 = shape223_out1[0] as i64;
        let shape224_out1: [i64; 3] = {
            let axes = &layernormalization60_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather187_out1 = shape224_out1[1] as i64;
        let linear118_out1 = self.linear118.forward(layernormalization60_out1);
        let unsqueeze552_out1 = [gather186_out1 as i64];
        let unsqueeze553_out1 = [gather187_out1 as i64];
        let constant2689_out1: [i64; 1] = [64i64];
        let constant2687_out1: [i64; 1] = [3i64];
        let constant2688_out1: [i64; 1] = [8i64];
        let concat244_out1: [i64; 5usize] = [
            &unsqueeze552_out1[..],
            &unsqueeze553_out1[..],
            &constant2687_out1[..],
            &constant2688_out1[..],
            &constant2689_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape185_out1 = linear118_out1.reshape(concat244_out1);
        let transpose88_out1 = reshape185_out1.permute([2, 0, 3, 1, 4]);
        let split_tensors = transpose88_out1.split_with_sizes([1, 1, 1].into(), 0);
        let [split30_out1, split30_out2, split30_out3] = split_tensors
            .try_into()
            .unwrap();
        let squeeze88_out1 = split30_out1.squeeze_dims::<4>(&[0]);
        let squeeze89_out1 = split30_out2.squeeze_dims::<4>(&[0]);
        let squeeze90_out1 = split30_out3.squeeze_dims::<4>(&[0]);
        let shape225_out1: [i64; 4] = {
            let axes = &squeeze88_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather188_out1 = shape225_out1[2] as i64;
        let shape226_out1: [i64; 4] = {
            let axes = &squeeze89_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather189_out1 = shape226_out1[2] as i64;
        let sub32_out1 = gather189_out1 - gather188_out1;
        let unsqueeze554_out1 = [gather189_out1 as i64];
        let slice266_out1 = constant461_out1
            .clone()
            .slice(s![0..unsqueeze554_out1[0], ..]);
        let unsqueeze555_out1 = [gather189_out1 as i64];
        let constant2703_out1: [i64; 1] = [64i64];
        let constant2700_out1: [i64; 1] = [1i64];
        let constant2701_out1: [i64; 1] = [1i64];
        let concat245_out1: [i64; 4usize] = [
            &constant2700_out1[..],
            &constant2701_out1[..],
            &unsqueeze555_out1[..],
            &constant2703_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze556_out1 = [gather189_out1 as i64];
        let constant2707_out1: [i64; 1] = [64i64];
        let constant2704_out1: [i64; 1] = [1i64];
        let constant2705_out1: [i64; 1] = [1i64];
        let concat246_out1: [i64; 4usize] = [
            &constant2704_out1[..],
            &constant2705_out1[..],
            &unsqueeze556_out1[..],
            &constant2707_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape186_out1 = slice266_out1.reshape(concat245_out1);
        let unsqueeze557_out1 = [gather189_out1 as i64];
        let slice267_out1 = constant474_out1
            .clone()
            .slice(s![0..unsqueeze557_out1[0], ..]);
        let reshape187_out1 = slice267_out1.reshape(concat246_out1);
        let unsqueeze558_out1 = [sub32_out1 as i64];
        let unsqueeze559_out1 = [gather189_out1 as i64];
        let slice268_out1 = constant461_out1
            .slice(s![unsqueeze558_out1[0]..unsqueeze559_out1[0], ..]);
        let unsqueeze560_out1 = [gather188_out1 as i64];
        let constant2719_out1: [i64; 1] = [64i64];
        let constant2716_out1: [i64; 1] = [1i64];
        let constant2717_out1: [i64; 1] = [1i64];
        let concat247_out1: [i64; 4usize] = [
            &constant2716_out1[..],
            &constant2717_out1[..],
            &unsqueeze560_out1[..],
            &constant2719_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze561_out1 = [gather188_out1 as i64];
        let constant2723_out1: [i64; 1] = [64i64];
        let constant2720_out1: [i64; 1] = [1i64];
        let constant2721_out1: [i64; 1] = [1i64];
        let concat248_out1: [i64; 4usize] = [
            &constant2720_out1[..],
            &constant2721_out1[..],
            &unsqueeze561_out1[..],
            &constant2723_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape188_out1 = slice268_out1.reshape(concat247_out1);
        let unsqueeze562_out1 = [sub32_out1 as i64];
        let unsqueeze563_out1 = [gather189_out1 as i64];
        let slice269_out1 = constant474_out1
            .slice(s![unsqueeze562_out1[0]..unsqueeze563_out1[0], ..]);
        let reshape189_out1 = slice269_out1.reshape(concat248_out1);
        let cast304_out1 = reshape188_out1;
        let cast305_out1 = reshape189_out1;
        let mul242_out1 = squeeze88_out1.clone().mul(cast304_out1);
        let shape227_out1: [i64; 4] = {
            let axes = &squeeze88_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather190_out1 = shape227_out1[3] as i64;
        let constant2729_out1 = 2i64;
        let div119_out1 = gather190_out1 / constant2729_out1;
        let cast306_out1 = div119_out1;
        let cast307_out1 = cast306_out1;
        let unsqueeze564_out1 = [cast307_out1 as i64];
        let slice270_out1 = squeeze88_out1
            .clone()
            .slice(s![.., .., .., unsqueeze564_out1[0]..9223372036854775807]);
        let neg59_out1 = slice270_out1.neg();
        let unsqueeze565_out1 = [cast307_out1 as i64];
        let slice271_out1 = squeeze88_out1
            .slice(s![.., .., .., 0..unsqueeze565_out1[0]]);
        let concat249_out1 = burn::tensor::Tensor::cat(
            [neg59_out1, slice271_out1].into(),
            3,
        );
        let mul243_out1 = concat249_out1.mul(cast305_out1);
        let add188_out1 = mul242_out1.add(mul243_out1);
        let cast308_out1 = reshape186_out1;
        let cast309_out1 = reshape187_out1;
        let mul244_out1 = squeeze89_out1.clone().mul(cast308_out1);
        let shape228_out1: [i64; 4] = {
            let axes = &squeeze89_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather191_out1 = shape228_out1[3] as i64;
        let constant2739_out1 = 2i64;
        let div120_out1 = gather191_out1 / constant2739_out1;
        let cast310_out1 = div120_out1;
        let cast311_out1 = cast310_out1;
        let unsqueeze566_out1 = [cast311_out1 as i64];
        let slice272_out1 = squeeze89_out1
            .clone()
            .slice(s![.., .., .., unsqueeze566_out1[0]..9223372036854775807]);
        let neg60_out1 = slice272_out1.neg();
        let unsqueeze567_out1 = [cast311_out1 as i64];
        let slice273_out1 = squeeze89_out1
            .slice(s![.., .., .., 0..unsqueeze567_out1[0]]);
        let concat250_out1 = burn::tensor::Tensor::cat(
            [neg60_out1, slice273_out1].into(),
            3,
        );
        let mul245_out1 = concat250_out1.mul(cast309_out1);
        let add189_out1 = mul244_out1.add(mul245_out1);
        let shape229_out1: [i64; 4] = {
            let axes = &add188_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let slice274_out1: [i64; 1] = shape229_out1[3..4].try_into().unwrap();
        let cast312_out1 = {
            let shape_array = slice274_out1 as [i64; 1usize];
            let float_array: [f64; 1usize] = shape_array.map(|x| x as f64);
            Tensor::<
                1,
            >::from_data(
                TensorData::from(float_array),
                (&self.device, burn::tensor::DType::F32),
            )
        };
        let sqrt88_out1 = cast312_out1.sqrt();
        let constant2750_out1 = self.constant2750.val();
        let div121_out1 = constant2750_out1.div(sqrt88_out1);
        let cast313_out1 = div121_out1;
        let transpose89_out1 = add189_out1.permute([0, 1, 3, 2]);
        let sqrt89_out1 = cast313_out1.clone().sqrt();
        let mul246_out1 = add188_out1
            .mul((sqrt89_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let sqrt90_out1 = cast313_out1.sqrt();
        let mul247_out1 = transpose89_out1
            .mul((sqrt90_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let matmul177_out1 = mul246_out1.matmul(mul247_out1);
        let constant2752_out1 = self.constant2752.val();
        let constant2751_out1 = self.constant2751.val();
        let where65_out1 = constant2752_out1
            .unsqueeze_dims(&[0isize, 1isize, 2isize])
            .mask_where(
                less4_out1,
                constant2751_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let add190_out1 = matmul177_out1.add(where65_out1);
        let softmax30_out1 = burn::tensor::activation::softmax(add190_out1, 3);
        let isnan30_out1 = softmax30_out1.clone().is_nan();
        let constant2753_out1 = self.constant2753.val();
        let where66_out1 = softmax30_out1
            .mask_where(
                isnan30_out1,
                constant2753_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let matmul178_out1 = where66_out1.matmul(squeeze90_out1);
        let transpose90_out1 = matmul178_out1.permute([0, 2, 1, 3]);
        let unsqueeze568_out1 = [gather186_out1 as i64];
        let unsqueeze569_out1 = [gather187_out1 as i64];
        let constant2756_out1: [i64; 1] = [512i64];
        let concat251_out1: [i64; 3usize] = [
            &unsqueeze568_out1[..],
            &unsqueeze569_out1[..],
            &constant2756_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape190_out1 = transpose90_out1.reshape(concat251_out1);
        let linear119_out1 = self.linear119.forward(reshape190_out1);
        let add191_out1 = add187_out1.add(linear119_out1);
        add191_out1
    }
}
#[derive(Module, Debug)]
pub struct Submodule32 {
    layernormalization61: LayerNorm,
    linear120: Linear,
    constant2757: burn::module::Param<Tensor<1>>,
    constant2758: burn::module::Param<Tensor<1>>,
    constant2759: burn::module::Param<Tensor<1>>,
    linear121: Linear,
    layernormalization62: LayerNorm,
    linear122: Linear,
    constant2827: burn::module::Param<Tensor<1>>,
    constant2829: burn::module::Param<Tensor<1>>,
    constant2828: burn::module::Param<Tensor<1>>,
    constant2830: burn::module::Param<Tensor<1>>,
    linear123: Linear,
    #[module(skip)]
    device: Device,
}
impl Submodule32 {
    #[allow(unused_variables)]
    pub fn new(device: &Device) -> Self {
        let layernormalization61 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear120 = LinearConfig::new(512, 2048).with_bias(true).init(device);
        let constant2757: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1.4142135381698608f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant2758: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant2759: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([0.5f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let linear121 = LinearConfig::new(2048, 512).with_bias(true).init(device);
        let layernormalization62 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear122 = LinearConfig::new(512, 1536).with_bias(false).init(device);
        let constant2827: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant2829: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant2828: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let constant2830: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::zeros([1], (device, burn::tensor::DType::F32)),
            device.clone(),
            false,
            [1].into(),
        );
        let linear123 = LinearConfig::new(512, 512).with_bias(true).init(device);
        Self {
            layernormalization61,
            linear120,
            constant2757,
            constant2758,
            constant2759,
            linear121,
            layernormalization62,
            linear122,
            constant2827,
            constant2829,
            constant2828,
            constant2830,
            linear123,
            device: device.clone(),
        }
    }
    #[allow(clippy::let_and_return, clippy::approx_constant)]
    pub fn forward(
        &self,
        add191_out1: Tensor<3>,
        constant461_out1: Tensor<2>,
        constant474_out1: Tensor<2>,
        less4_out1: Tensor<4, Bool>,
    ) -> Tensor<3> {
        let layernormalization61_out1 = {
            let dtype = add191_out1.clone().dtype();
            self.layernormalization61
                .forward(add191_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let linear120_out1 = self.linear120.forward(layernormalization61_out1);
        let constant2757_out1 = self.constant2757.val();
        let div122_out1 = linear120_out1
            .clone()
            .div((constant2757_out1).unsqueeze_dims(&[0isize, 1isize]));
        let erf30_out1 = div122_out1.erf();
        let constant2758_out1 = self.constant2758.val();
        let add192_out1 = erf30_out1
            .add((constant2758_out1).unsqueeze_dims(&[0isize, 1isize]));
        let mul248_out1 = linear120_out1.mul(add192_out1);
        let constant2759_out1 = self.constant2759.val();
        let mul249_out1 = mul248_out1
            .mul((constant2759_out1).unsqueeze_dims(&[0isize, 1isize]));
        let linear121_out1 = self.linear121.forward(mul249_out1);
        let add193_out1 = add191_out1.add(linear121_out1);
        let layernormalization62_out1 = {
            let dtype = add193_out1.clone().dtype();
            self.layernormalization62
                .forward(add193_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let shape230_out1: [i64; 3] = {
            let axes = &layernormalization62_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather192_out1 = shape230_out1[0] as i64;
        let shape231_out1: [i64; 3] = {
            let axes = &layernormalization62_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather193_out1 = shape231_out1[1] as i64;
        let linear122_out1 = self.linear122.forward(layernormalization62_out1);
        let unsqueeze570_out1 = [gather192_out1 as i64];
        let unsqueeze571_out1 = [gather193_out1 as i64];
        let constant2766_out1: [i64; 1] = [64i64];
        let constant2764_out1: [i64; 1] = [3i64];
        let constant2765_out1: [i64; 1] = [8i64];
        let concat252_out1: [i64; 5usize] = [
            &unsqueeze570_out1[..],
            &unsqueeze571_out1[..],
            &constant2764_out1[..],
            &constant2765_out1[..],
            &constant2766_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape191_out1 = linear122_out1.reshape(concat252_out1);
        let transpose91_out1 = reshape191_out1.permute([2, 0, 3, 1, 4]);
        let split_tensors = transpose91_out1.split_with_sizes([1, 1, 1].into(), 0);
        let [split31_out1, split31_out2, split31_out3] = split_tensors
            .try_into()
            .unwrap();
        let squeeze91_out1 = split31_out1.squeeze_dims::<4>(&[0]);
        let squeeze92_out1 = split31_out2.squeeze_dims::<4>(&[0]);
        let squeeze93_out1 = split31_out3.squeeze_dims::<4>(&[0]);
        let shape232_out1: [i64; 4] = {
            let axes = &squeeze91_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather194_out1 = shape232_out1[2] as i64;
        let shape233_out1: [i64; 4] = {
            let axes = &squeeze92_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather195_out1 = shape233_out1[2] as i64;
        let sub33_out1 = gather195_out1 - gather194_out1;
        let unsqueeze572_out1 = [gather195_out1 as i64];
        let slice275_out1 = constant461_out1
            .clone()
            .slice(s![0..unsqueeze572_out1[0], ..]);
        let unsqueeze573_out1 = [gather195_out1 as i64];
        let constant2780_out1: [i64; 1] = [64i64];
        let constant2777_out1: [i64; 1] = [1i64];
        let constant2778_out1: [i64; 1] = [1i64];
        let concat253_out1: [i64; 4usize] = [
            &constant2777_out1[..],
            &constant2778_out1[..],
            &unsqueeze573_out1[..],
            &constant2780_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze574_out1 = [gather195_out1 as i64];
        let constant2784_out1: [i64; 1] = [64i64];
        let constant2781_out1: [i64; 1] = [1i64];
        let constant2782_out1: [i64; 1] = [1i64];
        let concat254_out1: [i64; 4usize] = [
            &constant2781_out1[..],
            &constant2782_out1[..],
            &unsqueeze574_out1[..],
            &constant2784_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape192_out1 = slice275_out1.reshape(concat253_out1);
        let unsqueeze575_out1 = [gather195_out1 as i64];
        let slice276_out1 = constant474_out1
            .clone()
            .slice(s![0..unsqueeze575_out1[0], ..]);
        let reshape193_out1 = slice276_out1.reshape(concat254_out1);
        let unsqueeze576_out1 = [sub33_out1 as i64];
        let unsqueeze577_out1 = [gather195_out1 as i64];
        let slice277_out1 = constant461_out1
            .slice(s![unsqueeze576_out1[0]..unsqueeze577_out1[0], ..]);
        let unsqueeze578_out1 = [gather194_out1 as i64];
        let constant2796_out1: [i64; 1] = [64i64];
        let constant2793_out1: [i64; 1] = [1i64];
        let constant2794_out1: [i64; 1] = [1i64];
        let concat255_out1: [i64; 4usize] = [
            &constant2793_out1[..],
            &constant2794_out1[..],
            &unsqueeze578_out1[..],
            &constant2796_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let unsqueeze579_out1 = [gather194_out1 as i64];
        let constant2800_out1: [i64; 1] = [64i64];
        let constant2797_out1: [i64; 1] = [1i64];
        let constant2798_out1: [i64; 1] = [1i64];
        let concat256_out1: [i64; 4usize] = [
            &constant2797_out1[..],
            &constant2798_out1[..],
            &unsqueeze579_out1[..],
            &constant2800_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape194_out1 = slice277_out1.reshape(concat255_out1);
        let unsqueeze580_out1 = [sub33_out1 as i64];
        let unsqueeze581_out1 = [gather195_out1 as i64];
        let slice278_out1 = constant474_out1
            .slice(s![unsqueeze580_out1[0]..unsqueeze581_out1[0], ..]);
        let reshape195_out1 = slice278_out1.reshape(concat256_out1);
        let cast314_out1 = reshape194_out1;
        let cast315_out1 = reshape195_out1;
        let mul250_out1 = squeeze91_out1.clone().mul(cast314_out1);
        let shape234_out1: [i64; 4] = {
            let axes = &squeeze91_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather196_out1 = shape234_out1[3] as i64;
        let constant2806_out1 = 2i64;
        let div123_out1 = gather196_out1 / constant2806_out1;
        let cast316_out1 = div123_out1;
        let cast317_out1 = cast316_out1;
        let unsqueeze582_out1 = [cast317_out1 as i64];
        let slice279_out1 = squeeze91_out1
            .clone()
            .slice(s![.., .., .., unsqueeze582_out1[0]..9223372036854775807]);
        let neg61_out1 = slice279_out1.neg();
        let unsqueeze583_out1 = [cast317_out1 as i64];
        let slice280_out1 = squeeze91_out1
            .slice(s![.., .., .., 0..unsqueeze583_out1[0]]);
        let concat257_out1 = burn::tensor::Tensor::cat(
            [neg61_out1, slice280_out1].into(),
            3,
        );
        let mul251_out1 = concat257_out1.mul(cast315_out1);
        let add194_out1 = mul250_out1.add(mul251_out1);
        let cast318_out1 = reshape192_out1;
        let cast319_out1 = reshape193_out1;
        let mul252_out1 = squeeze92_out1.clone().mul(cast318_out1);
        let shape235_out1: [i64; 4] = {
            let axes = &squeeze92_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather197_out1 = shape235_out1[3] as i64;
        let constant2816_out1 = 2i64;
        let div124_out1 = gather197_out1 / constant2816_out1;
        let cast320_out1 = div124_out1;
        let cast321_out1 = cast320_out1;
        let unsqueeze584_out1 = [cast321_out1 as i64];
        let slice281_out1 = squeeze92_out1
            .clone()
            .slice(s![.., .., .., unsqueeze584_out1[0]..9223372036854775807]);
        let neg62_out1 = slice281_out1.neg();
        let unsqueeze585_out1 = [cast321_out1 as i64];
        let slice282_out1 = squeeze92_out1
            .slice(s![.., .., .., 0..unsqueeze585_out1[0]]);
        let concat258_out1 = burn::tensor::Tensor::cat(
            [neg62_out1, slice282_out1].into(),
            3,
        );
        let mul253_out1 = concat258_out1.mul(cast319_out1);
        let add195_out1 = mul252_out1.add(mul253_out1);
        let shape236_out1: [i64; 4] = {
            let axes = &add194_out1.clone().dims()[0..4];
            let mut output = [0i64; 4];
            for i in 0..4 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let slice283_out1: [i64; 1] = shape236_out1[3..4].try_into().unwrap();
        let cast322_out1 = {
            let shape_array = slice283_out1 as [i64; 1usize];
            let float_array: [f64; 1usize] = shape_array.map(|x| x as f64);
            Tensor::<
                1,
            >::from_data(
                TensorData::from(float_array),
                (&self.device, burn::tensor::DType::F32),
            )
        };
        let sqrt91_out1 = cast322_out1.sqrt();
        let constant2827_out1 = self.constant2827.val();
        let div125_out1 = constant2827_out1.div(sqrt91_out1);
        let cast323_out1 = div125_out1;
        let transpose92_out1 = add195_out1.permute([0, 1, 3, 2]);
        let sqrt92_out1 = cast323_out1.clone().sqrt();
        let mul254_out1 = add194_out1
            .mul((sqrt92_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let sqrt93_out1 = cast323_out1.sqrt();
        let mul255_out1 = transpose92_out1
            .mul((sqrt93_out1).unsqueeze_dims(&[0isize, 1isize, 2isize]));
        let matmul183_out1 = mul254_out1.matmul(mul255_out1);
        let constant2829_out1 = self.constant2829.val();
        let constant2828_out1 = self.constant2828.val();
        let where67_out1 = constant2829_out1
            .unsqueeze_dims(&[0isize, 1isize, 2isize])
            .mask_where(
                less4_out1,
                constant2828_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let add196_out1 = matmul183_out1.add(where67_out1);
        let softmax31_out1 = burn::tensor::activation::softmax(add196_out1, 3);
        let isnan31_out1 = softmax31_out1.clone().is_nan();
        let constant2830_out1 = self.constant2830.val();
        let where68_out1 = softmax31_out1
            .mask_where(
                isnan31_out1,
                constant2830_out1.unsqueeze_dims(&[0isize, 1isize, 2isize]),
            );
        let matmul184_out1 = where68_out1.matmul(squeeze93_out1);
        let transpose93_out1 = matmul184_out1.permute([0, 2, 1, 3]);
        let unsqueeze586_out1 = [gather192_out1 as i64];
        let unsqueeze587_out1 = [gather193_out1 as i64];
        let constant2833_out1: [i64; 1] = [512i64];
        let concat259_out1: [i64; 3usize] = [
            &unsqueeze586_out1[..],
            &unsqueeze587_out1[..],
            &constant2833_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape196_out1 = transpose93_out1.reshape(concat259_out1);
        let linear123_out1 = self.linear123.forward(reshape196_out1);
        let add197_out1 = add193_out1.add(linear123_out1);
        add197_out1
    }
}
#[derive(Module, Debug)]
pub struct Submodule33 {
    layernormalization63: LayerNorm,
    linear124: Linear,
    constant2834: burn::module::Param<Tensor<1>>,
    constant2835: burn::module::Param<Tensor<1>>,
    constant2836: burn::module::Param<Tensor<1>>,
    linear125: Linear,
    layernormalization64: LayerNorm,
    linear126: Linear,
    constant2843: burn::module::Param<Tensor<1, Int>>,
    conv1d1: Conv1d,
    linear127: Linear,
    linear128: Linear,
    averagepool1d1: AvgPool1d,
    #[module(skip)]
    device: Device,
}
impl Submodule33 {
    #[allow(unused_variables)]
    pub fn new(device: &Device) -> Self {
        let layernormalization63 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear124 = LinearConfig::new(512, 2048).with_bias(true).init(device);
        let constant2834: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1.4142135381698608f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant2835: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([1f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let constant2836: burn::module::Param<Tensor<1>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
            >::from_data(
                burn::tensor::TensorData::from([0.5f64]),
                (device, burn::tensor::DType::F32),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let linear125 = LinearConfig::new(2048, 512).with_bias(true).init(device);
        let layernormalization64 = LayerNormConfig::new(512)
            .with_epsilon(0.000009999999747378752f64)
            .with_bias(true)
            .init(device);
        let linear126 = LinearConfig::new(512, 192).with_bias(true).init(device);
        let constant2843: burn::module::Param<Tensor<1, Int>> = burn::module::Param::uninitialized(
            burn::module::ParamId::new(),
            move |device, _require_grad| Tensor::<
                1,
                Int,
            >::from_data(
                burn::tensor::TensorData::from([-1i64]),
                (device, burn::tensor::DType::I64),
            ),
            device.clone(),
            false,
            [1].into(),
        );
        let conv1d1 = Conv1dConfig::new(192, 1536, 3)
            .with_stride(1)
            .with_padding(PaddingConfig1d::Explicit(1, 1))
            .with_dilation(1)
            .with_groups(1)
            .with_bias(true)
            .init(device);
        let linear127 = LinearConfig::new(192, 192).with_bias(true).init(device);
        let linear128 = LinearConfig::new(192, 8).with_bias(true).init(device);
        let averagepool1d1 = AvgPool1dConfig::new(8)
            .with_stride(8)
            .with_padding(PaddingConfig1d::Valid)
            .with_count_include_pad(false)
            .with_ceil_mode(true)
            .init();
        Self {
            layernormalization63,
            linear124,
            constant2834,
            constant2835,
            constant2836,
            linear125,
            layernormalization64,
            linear126,
            constant2843,
            conv1d1,
            linear127,
            linear128,
            averagepool1d1,
            device: device.clone(),
        }
    }
    #[allow(clippy::let_and_return, clippy::approx_constant)]
    pub fn forward(
        &self,
        add197_out1: Tensor<3>,
        cast11_out1: Tensor<1, Int>,
    ) -> (Tensor<3>, Tensor<3>) {
        let layernormalization63_out1 = {
            let dtype = add197_out1.clone().dtype();
            self.layernormalization63
                .forward(add197_out1.clone().cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let linear124_out1 = self.linear124.forward(layernormalization63_out1);
        let constant2834_out1 = self.constant2834.val();
        let div126_out1 = linear124_out1
            .clone()
            .div((constant2834_out1).unsqueeze_dims(&[0isize, 1isize]));
        let erf31_out1 = div126_out1.erf();
        let constant2835_out1 = self.constant2835.val();
        let add198_out1 = erf31_out1
            .add((constant2835_out1).unsqueeze_dims(&[0isize, 1isize]));
        let mul256_out1 = linear124_out1.mul(add198_out1);
        let constant2836_out1 = self.constant2836.val();
        let mul257_out1 = mul256_out1
            .mul((constant2836_out1).unsqueeze_dims(&[0isize, 1isize]));
        let linear125_out1 = self.linear125.forward(mul257_out1);
        let add199_out1 = add197_out1.add(linear125_out1);
        let layernormalization64_out1 = {
            let dtype = add199_out1.dtype();
            self.layernormalization64
                .forward(add199_out1.cast(burn::tensor::DType::F32))
                .cast(dtype)
        };
        let cast324_out1 = cast11_out1;
        let linear126_out1 = self.linear126.forward(layernormalization64_out1);
        let shape237_out1: [i64; 3] = {
            let axes = &linear126_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather198_out1 = shape237_out1[1] as i64;
        let cast325_out1 = gather198_out1;
        let range7_out1 = {
            let start = 0i64;
            let delta = 1i64;
            assert!(delta != 0);
            let n = (((cast325_out1 as i128 - start as i128) as f64) / delta as f64)
                .ceil()
                .max(0.0) as i64;
            Tensor::arange(0..n, &self.device)
                .cast(burn::tensor::DType::I64)
                .mul_scalar(delta)
                .add_scalar(start)
        };
        let unsqueeze588_out1 = [gather198_out1 as i64];
        let constant2840_out1: [i64; 1] = [1i64];
        let concat260_out1: [i64; 2usize] = [
            &constant2840_out1[..],
            &unsqueeze588_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape197_out1 = concat260_out1;
        let shape238_out1: [i64; 1] = [2i64];
        let constantofshape6_out1 = Tensor::<
            1,
            Int,
        >::from_data(
                burn::tensor::TensorData::from([1i64 as i64]),
                (&self.device, burn::tensor::DType::I64),
            )
            .reshape([1])
            .expand(shape238_out1);
        let constant2843_out1 = self.constant2843.val();
        let mul258_out1 = constantofshape6_out1.clone().mul(constant2843_out1);
        let equal4_out1 = Tensor::<
            1,
            burn::tensor::Int,
        >::from_data(
                burn::tensor::TensorData::from(&reshape197_out1 as &[i64]),
                (&self.device, burn::tensor::DType::I64),
            )
            .equal(mul258_out1);
        let where69_out1 = Tensor::<
            1,
            burn::tensor::Int,
        >::from_data(
                burn::tensor::TensorData::from(&reshape197_out1 as &[i64]),
                (&self.device, burn::tensor::DType::I64),
            )
            .mask_where(equal4_out1, constantofshape6_out1);
        let constant2844_out1 = -1i64;
        let mul259_out1 = {
            let mut result = reshape197_out1;
            let scalar = constant2844_out1;
            for result_item in result.iter_mut() {
                *result_item = result_item.saturating_mul(scalar);
            }
            result
        };
        let max4_out1 = {
            let __lhs = reshape197_out1;
            let __rhs = mul259_out1;
            core::array::from_fn::<i64, 2usize, _>(|__i| (__lhs[__i]).max(__rhs[__i]))
        };
        let expand4_out1 = {
            let onnx_shape: [i64; 2usize] = max4_out1;
            let input_dims = range7_out1.dims();
            let mut shape = onnx_shape;
            #[allow(clippy::needless_range_loop)]
            for i in 0..1usize {
                let dim_offset = 1usize + i;
                if shape[dim_offset] == 1 && input_dims[i] > 1 {
                    shape[dim_offset] = input_dims[i] as i64;
                }
            }
            range7_out1.expand(shape)
        };
        let unsqueeze589_out1: Tensor<2, Int> = cast324_out1.unsqueeze_dims::<2>(&[1]);
        let less5_out1 = expand4_out1.lower(unsqueeze589_out1);
        let shape239_out1: [i64; 3] = {
            let axes = &linear126_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather199_out1 = shape239_out1[0] as i64;
        let shape240_out1: [i64; 3] = {
            let axes = &linear126_out1.clone().dims()[0..3];
            let mut output = [0i64; 3];
            for i in 0..3 {
                output[i] = axes[i] as i64;
            }
            output
        };
        let gather200_out1 = shape240_out1[2] as i64;
        let transpose94_out1 = linear126_out1.permute([0, 2, 1]);
        let conv1d1_out1 = self.conv1d1.forward(transpose94_out1);
        let transpose95_out1 = conv1d1_out1.permute([0, 2, 1]);
        let unsqueeze590_out1 = [gather199_out1 as i64];
        let unsqueeze591_out1 = [gather198_out1 as i64];
        let unsqueeze592_out1 = [gather200_out1 as i64];
        let constant2850_out1: [i64; 1] = [8i64];
        let concat261_out1: [i64; 4usize] = [
            &unsqueeze590_out1[..],
            &unsqueeze591_out1[..],
            &constant2850_out1[..],
            &unsqueeze592_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape198_out1 = transpose95_out1.reshape(concat261_out1);
        let constant2852_out1 = 8i64;
        let mul260_out1 = gather198_out1 * constant2852_out1;
        let unsqueeze593_out1 = [gather199_out1 as i64];
        let unsqueeze594_out1 = [mul260_out1 as i64];
        let unsqueeze595_out1 = [gather200_out1 as i64];
        let concat262_out1: [i64; 3usize] = [
            &unsqueeze593_out1[..],
            &unsqueeze594_out1[..],
            &unsqueeze595_out1[..],
        ]
            .concat()
            .try_into()
            .unwrap();
        let reshape199_out1 = reshape198_out1.reshape(concat262_out1);
        let unsqueeze596_out1: Tensor<3, Bool> = less5_out1.unsqueeze_dims::<3>(&[2]);
        let tile1_out1 = unsqueeze596_out1.repeat(&[1, 1, 8]);
        let flatten1_out1 = {
            let leading_dim = tile1_out1.shape()[..1].iter().product::<usize>() as i32;
            tile1_out1.reshape::<2, _>([leading_dim, -1])
        };
        let relu1_out1 = burn::tensor::activation::relu(reshape199_out1);
        let linear127_out1 = self.linear127.forward(relu1_out1);
        let relu2_out1 = burn::tensor::activation::relu(linear127_out1);
        let linear128_out1 = self.linear128.forward(relu2_out1);
        let sigmoid1_out1 = burn::tensor::activation::sigmoid(linear128_out1);
        let unsqueeze597_out1: Tensor<3, Bool> = flatten1_out1
            .unsqueeze_dims::<3>(&[-1]);
        let cast326_out1 = unsqueeze597_out1.float().cast(burn::tensor::DType::F32);
        let mul261_out1 = sigmoid1_out1.mul(cast326_out1);
        let transpose96_out1 = mul261_out1.clone().permute([0, 2, 1]);
        let averagepool1d1_out1 = self.averagepool1d1.forward(transpose96_out1);
        let transpose97_out1 = averagepool1d1_out1.permute([0, 2, 1]);
        (transpose97_out1, mul261_out1)
    }
}

#[derive(Module, Debug)]
pub struct Model {
    submodule1: Submodule1,
    submodule2: Submodule2,
    submodule3: Submodule3,
    submodule4: Submodule4,
    submodule5: Submodule5,
    submodule6: Submodule6,
    submodule7: Submodule7,
    submodule8: Submodule8,
    submodule9: Submodule9,
    submodule10: Submodule10,
    submodule11: Submodule11,
    submodule12: Submodule12,
    submodule13: Submodule13,
    submodule14: Submodule14,
    submodule15: Submodule15,
    submodule16: Submodule16,
    submodule17: Submodule17,
    submodule18: Submodule18,
    submodule19: Submodule19,
    submodule20: Submodule20,
    submodule21: Submodule21,
    submodule22: Submodule22,
    submodule23: Submodule23,
    submodule24: Submodule24,
    submodule25: Submodule25,
    submodule26: Submodule26,
    submodule27: Submodule27,
    submodule28: Submodule28,
    submodule29: Submodule29,
    submodule30: Submodule30,
    submodule31: Submodule31,
    submodule32: Submodule32,
    submodule33: Submodule33,
    #[module(skip)]
    device: Device,
}





impl Model {
    #[allow(unused_variables)]
    pub fn new(device: &Device) -> Self {
        let submodule1 = Submodule1::new(device);
        let submodule2 = Submodule2::new(device);
        let submodule3 = Submodule3::new(device);
        let submodule4 = Submodule4::new(device);
        let submodule5 = Submodule5::new(device);
        let submodule6 = Submodule6::new(device);
        let submodule7 = Submodule7::new(device);
        let submodule8 = Submodule8::new(device);
        let submodule9 = Submodule9::new(device);
        let submodule10 = Submodule10::new(device);
        let submodule11 = Submodule11::new(device);
        let submodule12 = Submodule12::new(device);
        let submodule13 = Submodule13::new(device);
        let submodule14 = Submodule14::new(device);
        let submodule15 = Submodule15::new(device);
        let submodule16 = Submodule16::new(device);
        let submodule17 = Submodule17::new(device);
        let submodule18 = Submodule18::new(device);
        let submodule19 = Submodule19::new(device);
        let submodule20 = Submodule20::new(device);
        let submodule21 = Submodule21::new(device);
        let submodule22 = Submodule22::new(device);
        let submodule23 = Submodule23::new(device);
        let submodule24 = Submodule24::new(device);
        let submodule25 = Submodule25::new(device);
        let submodule26 = Submodule26::new(device);
        let submodule27 = Submodule27::new(device);
        let submodule28 = Submodule28::new(device);
        let submodule29 = Submodule29::new(device);
        let submodule30 = Submodule30::new(device);
        let submodule31 = Submodule31::new(device);
        let submodule32 = Submodule32::new(device);
        let submodule33 = Submodule33::new(device);
        Self {
            submodule1,
            submodule2,
            submodule3,
            submodule4,
            submodule5,
            submodule6,
            submodule7,
            submodule8,
            submodule9,
            submodule10,
            submodule11,
            submodule12,
            submodule13,
            submodule14,
            submodule15,
            submodule16,
            submodule17,
            submodule18,
            submodule19,
            submodule20,
            submodule21,
            submodule22,
            submodule23,
            submodule24,
            submodule25,
            submodule26,
            submodule27,
            submodule28,
            submodule29,
            submodule30,
            submodule31,
            submodule32,
            submodule33,
            device: device.clone(),
        }
    }

    #[allow(clippy::let_and_return, clippy::approx_constant)]
    pub fn forward(
        &self,
        chunk: Tensor<3>,
        chunk_lengths: Tensor<1, Int>,
        spkcache: Tensor<3>,
        spkcache_lengths: Tensor<1, Int>,
        fifo: Tensor<3>,
        fifo_lengths: Tensor<1, Int>,
    ) -> (Tensor<3>, Tensor<3>, Tensor<3>, Tensor<1, Int>) {
        let (reducesum1_out1, reshape8_out1, linear1_out1, cast5_out1) = self
            .submodule1
            .forward(
                chunk,
                chunk_lengths,
                spkcache,
                fifo,
                spkcache_lengths,
                fifo_lengths,
            );
        let (add17_out1, constant461_out1, constant474_out1, less4_out1, cast11_out1) = self
            .submodule2
            .forward(reducesum1_out1, reshape8_out1);
        let add23_out1 = self
            .submodule3
            .forward(
                add17_out1,
                constant461_out1.clone(),
                constant474_out1.clone(),
                less4_out1.clone(),
            );
        let add29_out1 = self
            .submodule4
            .forward(
                add23_out1,
                constant461_out1.clone(),
                constant474_out1.clone(),
                less4_out1.clone(),
            );
        let add35_out1 = self
            .submodule5
            .forward(
                add29_out1,
                constant461_out1.clone(),
                constant474_out1.clone(),
                less4_out1.clone(),
            );
        let add41_out1 = self
            .submodule6
            .forward(
                add35_out1,
                constant461_out1.clone(),
                constant474_out1.clone(),
                less4_out1.clone(),
            );
        let add47_out1 = self
            .submodule7
            .forward(
                add41_out1,
                constant461_out1.clone(),
                constant474_out1.clone(),
                less4_out1.clone(),
            );
        let add53_out1 = self
            .submodule8
            .forward(
                add47_out1,
                constant461_out1.clone(),
                constant474_out1.clone(),
                less4_out1.clone(),
            );
        let add59_out1 = self
            .submodule9
            .forward(
                add53_out1,
                constant461_out1.clone(),
                constant474_out1.clone(),
                less4_out1.clone(),
            );
        let add65_out1 = self
            .submodule10
            .forward(
                add59_out1,
                constant461_out1.clone(),
                constant474_out1.clone(),
                less4_out1.clone(),
            );
        let add71_out1 = self
            .submodule11
            .forward(
                add65_out1,
                constant461_out1.clone(),
                constant474_out1.clone(),
                less4_out1.clone(),
            );
        let add77_out1 = self
            .submodule12
            .forward(
                add71_out1,
                constant461_out1.clone(),
                constant474_out1.clone(),
                less4_out1.clone(),
            );
        let add83_out1 = self
            .submodule13
            .forward(
                add77_out1,
                constant461_out1.clone(),
                constant474_out1.clone(),
                less4_out1.clone(),
            );
        let add89_out1 = self
            .submodule14
            .forward(
                add83_out1,
                constant461_out1.clone(),
                constant474_out1.clone(),
                less4_out1.clone(),
            );
        let add95_out1 = self
            .submodule15
            .forward(
                add89_out1,
                constant461_out1.clone(),
                constant474_out1.clone(),
                less4_out1.clone(),
            );
        let add101_out1 = self
            .submodule16
            .forward(
                add95_out1,
                constant461_out1.clone(),
                constant474_out1.clone(),
                less4_out1.clone(),
            );
        let add107_out1 = self
            .submodule17
            .forward(
                add101_out1,
                constant461_out1.clone(),
                constant474_out1.clone(),
                less4_out1.clone(),
            );
        let add113_out1 = self
            .submodule18
            .forward(
                add107_out1,
                constant461_out1.clone(),
                constant474_out1.clone(),
                less4_out1.clone(),
            );
        let add119_out1 = self
            .submodule19
            .forward(
                add113_out1,
                constant461_out1.clone(),
                constant474_out1.clone(),
                less4_out1.clone(),
            );
        let add125_out1 = self
            .submodule20
            .forward(
                add119_out1,
                constant461_out1.clone(),
                constant474_out1.clone(),
                less4_out1.clone(),
            );
        let add131_out1 = self
            .submodule21
            .forward(
                add125_out1,
                constant461_out1.clone(),
                constant474_out1.clone(),
                less4_out1.clone(),
            );
        let add137_out1 = self
            .submodule22
            .forward(
                add131_out1,
                constant461_out1.clone(),
                constant474_out1.clone(),
                less4_out1.clone(),
            );
        let add143_out1 = self
            .submodule23
            .forward(
                add137_out1,
                constant461_out1.clone(),
                constant474_out1.clone(),
                less4_out1.clone(),
            );
        let add149_out1 = self
            .submodule24
            .forward(
                add143_out1,
                constant461_out1.clone(),
                constant474_out1.clone(),
                less4_out1.clone(),
            );
        let add155_out1 = self
            .submodule25
            .forward(
                add149_out1,
                constant461_out1.clone(),
                constant474_out1.clone(),
                less4_out1.clone(),
            );
        let add161_out1 = self
            .submodule26
            .forward(
                add155_out1,
                constant461_out1.clone(),
                constant474_out1.clone(),
                less4_out1.clone(),
            );
        let add167_out1 = self
            .submodule27
            .forward(
                add161_out1,
                constant461_out1.clone(),
                constant474_out1.clone(),
                less4_out1.clone(),
            );
        let add173_out1 = self
            .submodule28
            .forward(
                add167_out1,
                constant461_out1.clone(),
                constant474_out1.clone(),
                less4_out1.clone(),
            );
        let add179_out1 = self
            .submodule29
            .forward(
                add173_out1,
                constant461_out1.clone(),
                constant474_out1.clone(),
                less4_out1.clone(),
            );
        let add185_out1 = self
            .submodule30
            .forward(
                add179_out1,
                constant461_out1.clone(),
                constant474_out1.clone(),
                less4_out1.clone(),
            );
        let add191_out1 = self
            .submodule31
            .forward(
                add185_out1,
                constant461_out1.clone(),
                constant474_out1.clone(),
                less4_out1.clone(),
            );
        let add197_out1 = self
            .submodule32
            .forward(add191_out1, constant461_out1, constant474_out1, less4_out1);
        let (transpose97_out1, mul261_out1) = self
            .submodule33
            .forward(add197_out1, cast11_out1);
        (transpose97_out1, mul261_out1, linear1_out1, cast5_out1)
    }
}