opus1 0.2.1

//! BBWENet, the network of the bandwidth extension (BWE) of SILK wideband to 48
//! kHz: its layers and the constants of its C header.
//!
//! Generated by `tools/dnn-gen` from libopus 1.6.1 `dnn/bbwenet_data.h` and
//! `dnn/bbwenet_data.c` (`init_bbwenetlayers`). Do not edit: change the
//! generator and run it again (`tools/dnn-gen/README.md`).

// The float constants keep every digit of the C header.
#![allow(clippy::excessive_precision)]

use crate::Error;
use super::{Layer, LayerSpec, LinearSpec};
use crate::dnn::nnet::LinearLayer;
use crate::dnn::weights::WeightList;

/// The file of this model's weights in `dnn-weights/`.
pub const BLOB_FILE: &str = "bbwenet.bin";

// The constants of `dnn/bbwenet_data.h`, in the same order.

/// `BBWENET_FEATURE_DIM`.
pub const BBWENET_FEATURE_DIM: usize = 114;

/// `BBWENET_FRAME_SIZE16`.
pub const BBWENET_FRAME_SIZE16: usize = 80;

/// `BBWENET_COND_DIM`.
pub const BBWENET_COND_DIM: usize = 128;

/// `BBWENET_FNET_CONV1_OUT_SIZE`.
pub const BBWENET_FNET_CONV1_OUT_SIZE: usize = 128;

/// `BBWENET_FNET_CONV1_IN_SIZE`.
pub const BBWENET_FNET_CONV1_IN_SIZE: usize = 114;

/// `BBWENET_FNET_CONV1_STATE_SIZE`, `(114 * (2))` in C.
pub const BBWENET_FNET_CONV1_STATE_SIZE: usize = 228;

/// `BBWENET_FNET_CONV2_OUT_SIZE`.
pub const BBWENET_FNET_CONV2_OUT_SIZE: usize = 128;

/// `BBWENET_FNET_CONV2_IN_SIZE`.
pub const BBWENET_FNET_CONV2_IN_SIZE: usize = 128;

/// `BBWENET_FNET_CONV2_STATE_SIZE`, `(128 * (2))` in C.
pub const BBWENET_FNET_CONV2_STATE_SIZE: usize = 256;

/// `BBWENET_FNET_GRU_OUT_SIZE`.
pub const BBWENET_FNET_GRU_OUT_SIZE: usize = 128;

/// `BBWENET_FNET_GRU_STATE_SIZE`.
pub const BBWENET_FNET_GRU_STATE_SIZE: usize = 128;

/// `BBWENET_FNET_TCONV_KERNEL_SIZE`.
pub const BBWENET_FNET_TCONV_KERNEL_SIZE: usize = 2;

/// `BBWENET_FNET_TCONV_STRIDE`.
pub const BBWENET_FNET_TCONV_STRIDE: usize = 2;

/// `BBWENET_FNET_TCONV_IN_CHANNELS`.
pub const BBWENET_FNET_TCONV_IN_CHANNELS: usize = 128;

/// `BBWENET_FNET_TCONV_OUT_CHANNELS`.
pub const BBWENET_FNET_TCONV_OUT_CHANNELS: usize = 128;

/// `BBWENET_TDSHAPE1_FEATURE_DIM`.
pub const BBWENET_TDSHAPE1_FEATURE_DIM: usize = 128;

/// `BBWENET_TDSHAPE1_FRAME_SIZE`.
pub const BBWENET_TDSHAPE1_FRAME_SIZE: usize = 160;

/// `BBWENET_TDSHAPE1_AVG_POOL_K`.
pub const BBWENET_TDSHAPE1_AVG_POOL_K: usize = 8;

/// `BBWENET_TDSHAPE1_INNOVATE`.
pub const BBWENET_TDSHAPE1_INNOVATE: usize = 0;

/// `BBWENET_TDSHAPE1_POOL_AFTER`.
pub const BBWENET_TDSHAPE1_POOL_AFTER: usize = 0;

/// `BBWENET_TDSHAPE1_INTERPOLATE_K`.
pub const BBWENET_TDSHAPE1_INTERPOLATE_K: usize = 2;

/// `BBWENET_TDSHAPE1_ALPHA1_F_OUT_SIZE`.
pub const BBWENET_TDSHAPE1_ALPHA1_F_OUT_SIZE: usize = 80;

/// `BBWENET_TDSHAPE1_ALPHA1_F_IN_SIZE`.
pub const BBWENET_TDSHAPE1_ALPHA1_F_IN_SIZE: usize = 128;

/// `BBWENET_TDSHAPE1_ALPHA1_F_STATE_SIZE`, `(128 * (1))` in C.
pub const BBWENET_TDSHAPE1_ALPHA1_F_STATE_SIZE: usize = 128;

/// `BBWENET_TDSHAPE1_ALPHA1_T_OUT_SIZE`.
pub const BBWENET_TDSHAPE1_ALPHA1_T_OUT_SIZE: usize = 80;

/// `BBWENET_TDSHAPE1_ALPHA1_T_IN_SIZE`.
pub const BBWENET_TDSHAPE1_ALPHA1_T_IN_SIZE: usize = 21;

/// `BBWENET_TDSHAPE1_ALPHA1_T_STATE_SIZE`, `(21 * (1))` in C.
pub const BBWENET_TDSHAPE1_ALPHA1_T_STATE_SIZE: usize = 21;

/// `BBWENET_TDSHAPE1_ALPHA2_OUT_SIZE`.
pub const BBWENET_TDSHAPE1_ALPHA2_OUT_SIZE: usize = 80;

/// `BBWENET_TDSHAPE1_ALPHA2_IN_SIZE`.
pub const BBWENET_TDSHAPE1_ALPHA2_IN_SIZE: usize = 80;

/// `BBWENET_TDSHAPE1_ALPHA2_STATE_SIZE`, `(80 * (1))` in C.
pub const BBWENET_TDSHAPE1_ALPHA2_STATE_SIZE: usize = 80;

/// `BBWENET_TDSHAPE2_FEATURE_DIM`.
pub const BBWENET_TDSHAPE2_FEATURE_DIM: usize = 128;

/// `BBWENET_TDSHAPE2_FRAME_SIZE`.
pub const BBWENET_TDSHAPE2_FRAME_SIZE: usize = 240;

/// `BBWENET_TDSHAPE2_AVG_POOL_K`.
pub const BBWENET_TDSHAPE2_AVG_POOL_K: usize = 12;

/// `BBWENET_TDSHAPE2_INNOVATE`.
pub const BBWENET_TDSHAPE2_INNOVATE: usize = 0;

/// `BBWENET_TDSHAPE2_POOL_AFTER`.
pub const BBWENET_TDSHAPE2_POOL_AFTER: usize = 0;

/// `BBWENET_TDSHAPE2_INTERPOLATE_K`.
pub const BBWENET_TDSHAPE2_INTERPOLATE_K: usize = 2;

/// `BBWENET_TDSHAPE2_ALPHA1_F_OUT_SIZE`.
pub const BBWENET_TDSHAPE2_ALPHA1_F_OUT_SIZE: usize = 120;

/// `BBWENET_TDSHAPE2_ALPHA1_F_IN_SIZE`.
pub const BBWENET_TDSHAPE2_ALPHA1_F_IN_SIZE: usize = 128;

/// `BBWENET_TDSHAPE2_ALPHA1_F_STATE_SIZE`, `(128 * (1))` in C.
pub const BBWENET_TDSHAPE2_ALPHA1_F_STATE_SIZE: usize = 128;

/// `BBWENET_TDSHAPE2_ALPHA1_T_OUT_SIZE`.
pub const BBWENET_TDSHAPE2_ALPHA1_T_OUT_SIZE: usize = 120;

/// `BBWENET_TDSHAPE2_ALPHA1_T_IN_SIZE`.
pub const BBWENET_TDSHAPE2_ALPHA1_T_IN_SIZE: usize = 21;

/// `BBWENET_TDSHAPE2_ALPHA1_T_STATE_SIZE`, `(21 * (1))` in C.
pub const BBWENET_TDSHAPE2_ALPHA1_T_STATE_SIZE: usize = 21;

/// `BBWENET_TDSHAPE2_ALPHA2_OUT_SIZE`.
pub const BBWENET_TDSHAPE2_ALPHA2_OUT_SIZE: usize = 120;

/// `BBWENET_TDSHAPE2_ALPHA2_IN_SIZE`.
pub const BBWENET_TDSHAPE2_ALPHA2_IN_SIZE: usize = 120;

/// `BBWENET_TDSHAPE2_ALPHA2_STATE_SIZE`, `(120 * (1))` in C.
pub const BBWENET_TDSHAPE2_ALPHA2_STATE_SIZE: usize = 120;

/// `BBWENET_AF1_FILTER_GAIN_A`, `1.381551f` in C.
pub const BBWENET_AF1_FILTER_GAIN_A: f32 = 1.381551;

/// `BBWENET_AF1_FILTER_GAIN_B`, `0.000000f` in C.
pub const BBWENET_AF1_FILTER_GAIN_B: f32 = 0.000000;

/// `BBWENET_AF1_SHAPE_GAIN`, `1.000000f` in C.
pub const BBWENET_AF1_SHAPE_GAIN: f32 = 1.000000;

/// `BBWENET_AF1_KERNEL_SIZE`.
pub const BBWENET_AF1_KERNEL_SIZE: usize = 16;

/// `BBWENET_AF1_FRAME_SIZE`.
pub const BBWENET_AF1_FRAME_SIZE: usize = 80;

/// `BBWENET_AF1_LEFT_PADDING`.
pub const BBWENET_AF1_LEFT_PADDING: usize = 15;

/// `BBWENET_AF1_OVERLAP_SIZE`.
pub const BBWENET_AF1_OVERLAP_SIZE: usize = 40;

/// `BBWENET_AF1_IN_CHANNELS`.
pub const BBWENET_AF1_IN_CHANNELS: usize = 1;

/// `BBWENET_AF1_OUT_CHANNELS`.
pub const BBWENET_AF1_OUT_CHANNELS: usize = 3;

/// `BBWENET_AF1_NORM_P`.
pub const BBWENET_AF1_NORM_P: usize = 2;

/// `BBWENET_AF1_FEATURE_DIM`.
pub const BBWENET_AF1_FEATURE_DIM: usize = 128;

/// `BBWENET_AF1_KERNEL_OUT_SIZE`.
pub const BBWENET_AF1_KERNEL_OUT_SIZE: usize = 48;

/// `BBWENET_AF1_GAIN_OUT_SIZE`.
pub const BBWENET_AF1_GAIN_OUT_SIZE: usize = 3;

/// `BBWENET_AF2_FILTER_GAIN_A`, `1.381551f` in C.
pub const BBWENET_AF2_FILTER_GAIN_A: f32 = 1.381551;

/// `BBWENET_AF2_FILTER_GAIN_B`, `0.000000f` in C.
pub const BBWENET_AF2_FILTER_GAIN_B: f32 = 0.000000;

/// `BBWENET_AF2_SHAPE_GAIN`, `1.000000f` in C.
pub const BBWENET_AF2_SHAPE_GAIN: f32 = 1.000000;

/// `BBWENET_AF2_KERNEL_SIZE`.
pub const BBWENET_AF2_KERNEL_SIZE: usize = 32;

/// `BBWENET_AF2_FRAME_SIZE`.
pub const BBWENET_AF2_FRAME_SIZE: usize = 160;

/// `BBWENET_AF2_LEFT_PADDING`.
pub const BBWENET_AF2_LEFT_PADDING: usize = 31;

/// `BBWENET_AF2_OVERLAP_SIZE`.
pub const BBWENET_AF2_OVERLAP_SIZE: usize = 80;

/// `BBWENET_AF2_IN_CHANNELS`.
pub const BBWENET_AF2_IN_CHANNELS: usize = 3;

/// `BBWENET_AF2_OUT_CHANNELS`.
pub const BBWENET_AF2_OUT_CHANNELS: usize = 3;

/// `BBWENET_AF2_NORM_P`.
pub const BBWENET_AF2_NORM_P: usize = 2;

/// `BBWENET_AF2_FEATURE_DIM`.
pub const BBWENET_AF2_FEATURE_DIM: usize = 128;

/// `BBWENET_AF2_KERNEL_OUT_SIZE`.
pub const BBWENET_AF2_KERNEL_OUT_SIZE: usize = 288;

/// `BBWENET_AF2_GAIN_OUT_SIZE`.
pub const BBWENET_AF2_GAIN_OUT_SIZE: usize = 3;

/// `BBWENET_AF3_FILTER_GAIN_A`, `1.381551f` in C.
pub const BBWENET_AF3_FILTER_GAIN_A: f32 = 1.381551;

/// `BBWENET_AF3_FILTER_GAIN_B`, `0.000000f` in C.
pub const BBWENET_AF3_FILTER_GAIN_B: f32 = 0.000000;

/// `BBWENET_AF3_SHAPE_GAIN`, `1.000000f` in C.
pub const BBWENET_AF3_SHAPE_GAIN: f32 = 1.000000;

/// `BBWENET_AF3_KERNEL_SIZE`.
pub const BBWENET_AF3_KERNEL_SIZE: usize = 16;

/// `BBWENET_AF3_FRAME_SIZE`.
pub const BBWENET_AF3_FRAME_SIZE: usize = 240;

/// `BBWENET_AF3_LEFT_PADDING`.
pub const BBWENET_AF3_LEFT_PADDING: usize = 15;

/// `BBWENET_AF3_OVERLAP_SIZE`.
pub const BBWENET_AF3_OVERLAP_SIZE: usize = 120;

/// `BBWENET_AF3_IN_CHANNELS`.
pub const BBWENET_AF3_IN_CHANNELS: usize = 3;

/// `BBWENET_AF3_OUT_CHANNELS`.
pub const BBWENET_AF3_OUT_CHANNELS: usize = 1;

/// `BBWENET_AF3_NORM_P`.
pub const BBWENET_AF3_NORM_P: usize = 2;

/// `BBWENET_AF3_FEATURE_DIM`.
pub const BBWENET_AF3_FEATURE_DIM: usize = 128;

/// `BBWENET_AF3_KERNEL_OUT_SIZE`.
pub const BBWENET_AF3_KERNEL_OUT_SIZE: usize = 48;

/// `BBWENET_AF3_GAIN_OUT_SIZE`.
pub const BBWENET_AF3_GAIN_OUT_SIZE: usize = 1;

/// The layers in the order of `init_bbwenetlayers`: the arrays that each one
/// reads, and its shape. The names are the array names of the blob.
pub const LAYERS: [LayerSpec; 17] = [
    LayerSpec::Linear(LinearSpec {
        name: "bbwenet_fnet_conv1",
        bias: Some("bbwenet_fnet_conv1_bias"),
        subias: None,
        weights: None,
        float_weights: Some("bbwenet_fnet_conv1_weights_float"),
        weights_idx: None,
        diag: None,
        scale: None,
        nb_inputs: 342,
        nb_outputs: 128,
    }),
    LayerSpec::Linear(LinearSpec {
        name: "bbwenet_fnet_conv2",
        bias: Some("bbwenet_fnet_conv2_bias"),
        subias: Some("bbwenet_fnet_conv2_subias"),
        weights: Some("bbwenet_fnet_conv2_weights_int8"),
        float_weights: Some("bbwenet_fnet_conv2_weights_float"),
        weights_idx: None,
        diag: None,
        scale: Some("bbwenet_fnet_conv2_scale"),
        nb_inputs: 384,
        nb_outputs: 128,
    }),
    LayerSpec::Linear(LinearSpec {
        name: "bbwenet_fnet_gru_input",
        bias: Some("bbwenet_fnet_gru_input_bias"),
        subias: Some("bbwenet_fnet_gru_input_subias"),
        weights: Some("bbwenet_fnet_gru_input_weights_int8"),
        float_weights: Some("bbwenet_fnet_gru_input_weights_float"),
        weights_idx: None,
        diag: None,
        scale: Some("bbwenet_fnet_gru_input_scale"),
        nb_inputs: 128,
        nb_outputs: 384,
    }),
    LayerSpec::Linear(LinearSpec {
        name: "bbwenet_fnet_gru_recurrent",
        bias: Some("bbwenet_fnet_gru_recurrent_bias"),
        subias: Some("bbwenet_fnet_gru_recurrent_subias"),
        weights: Some("bbwenet_fnet_gru_recurrent_weights_int8"),
        float_weights: Some("bbwenet_fnet_gru_recurrent_weights_float"),
        weights_idx: None,
        diag: None,
        scale: Some("bbwenet_fnet_gru_recurrent_scale"),
        nb_inputs: 128,
        nb_outputs: 384,
    }),
    LayerSpec::Linear(LinearSpec {
        name: "bbwenet_fnet_tconv",
        bias: Some("bbwenet_fnet_tconv_bias"),
        subias: Some("bbwenet_fnet_tconv_subias"),
        weights: Some("bbwenet_fnet_tconv_weights_int8"),
        float_weights: Some("bbwenet_fnet_tconv_weights_float"),
        weights_idx: None,
        diag: None,
        scale: Some("bbwenet_fnet_tconv_scale"),
        nb_inputs: 128,
        nb_outputs: 256,
    }),
    LayerSpec::Linear(LinearSpec {
        name: "bbwenet_tdshape1_alpha1_f",
        bias: Some("bbwenet_tdshape1_alpha1_f_bias"),
        subias: Some("bbwenet_tdshape1_alpha1_f_subias"),
        weights: Some("bbwenet_tdshape1_alpha1_f_weights_int8"),
        float_weights: Some("bbwenet_tdshape1_alpha1_f_weights_float"),
        weights_idx: None,
        diag: None,
        scale: Some("bbwenet_tdshape1_alpha1_f_scale"),
        nb_inputs: 256,
        nb_outputs: 80,
    }),
    LayerSpec::Linear(LinearSpec {
        name: "bbwenet_tdshape1_alpha1_t",
        bias: Some("bbwenet_tdshape1_alpha1_t_bias"),
        subias: None,
        weights: None,
        float_weights: Some("bbwenet_tdshape1_alpha1_t_weights_float"),
        weights_idx: None,
        diag: None,
        scale: None,
        nb_inputs: 42,
        nb_outputs: 80,
    }),
    LayerSpec::Linear(LinearSpec {
        name: "bbwenet_tdshape1_alpha2",
        bias: Some("bbwenet_tdshape1_alpha2_bias"),
        subias: None,
        weights: None,
        float_weights: Some("bbwenet_tdshape1_alpha2_weights_float"),
        weights_idx: None,
        diag: None,
        scale: None,
        nb_inputs: 160,
        nb_outputs: 80,
    }),
    LayerSpec::Linear(LinearSpec {
        name: "bbwenet_tdshape2_alpha1_f",
        bias: Some("bbwenet_tdshape2_alpha1_f_bias"),
        subias: Some("bbwenet_tdshape2_alpha1_f_subias"),
        weights: Some("bbwenet_tdshape2_alpha1_f_weights_int8"),
        float_weights: Some("bbwenet_tdshape2_alpha1_f_weights_float"),
        weights_idx: None,
        diag: None,
        scale: Some("bbwenet_tdshape2_alpha1_f_scale"),
        nb_inputs: 256,
        nb_outputs: 120,
    }),
    LayerSpec::Linear(LinearSpec {
        name: "bbwenet_tdshape2_alpha1_t",
        bias: Some("bbwenet_tdshape2_alpha1_t_bias"),
        subias: None,
        weights: None,
        float_weights: Some("bbwenet_tdshape2_alpha1_t_weights_float"),
        weights_idx: None,
        diag: None,
        scale: None,
        nb_inputs: 42,
        nb_outputs: 120,
    }),
    LayerSpec::Linear(LinearSpec {
        name: "bbwenet_tdshape2_alpha2",
        bias: Some("bbwenet_tdshape2_alpha2_bias"),
        subias: None,
        weights: None,
        float_weights: Some("bbwenet_tdshape2_alpha2_weights_float"),
        weights_idx: None,
        diag: None,
        scale: None,
        nb_inputs: 240,
        nb_outputs: 120,
    }),
    LayerSpec::Linear(LinearSpec {
        name: "bbwenet_af1_kernel",
        bias: Some("bbwenet_af1_kernel_bias"),
        subias: Some("bbwenet_af1_kernel_subias"),
        weights: Some("bbwenet_af1_kernel_weights_int8"),
        float_weights: Some("bbwenet_af1_kernel_weights_float"),
        weights_idx: None,
        diag: None,
        scale: Some("bbwenet_af1_kernel_scale"),
        nb_inputs: 128,
        nb_outputs: 48,
    }),
    LayerSpec::Linear(LinearSpec {
        name: "bbwenet_af1_gain",
        bias: Some("bbwenet_af1_gain_bias"),
        subias: None,
        weights: None,
        float_weights: Some("bbwenet_af1_gain_weights_float"),
        weights_idx: None,
        diag: None,
        scale: None,
        nb_inputs: 128,
        nb_outputs: 3,
    }),
    LayerSpec::Linear(LinearSpec {
        name: "bbwenet_af2_kernel",
        bias: Some("bbwenet_af2_kernel_bias"),
        subias: Some("bbwenet_af2_kernel_subias"),
        weights: Some("bbwenet_af2_kernel_weights_int8"),
        float_weights: Some("bbwenet_af2_kernel_weights_float"),
        weights_idx: None,
        diag: None,
        scale: Some("bbwenet_af2_kernel_scale"),
        nb_inputs: 128,
        nb_outputs: 288,
    }),
    LayerSpec::Linear(LinearSpec {
        name: "bbwenet_af2_gain",
        bias: Some("bbwenet_af2_gain_bias"),
        subias: None,
        weights: None,
        float_weights: Some("bbwenet_af2_gain_weights_float"),
        weights_idx: None,
        diag: None,
        scale: None,
        nb_inputs: 128,
        nb_outputs: 3,
    }),
    LayerSpec::Linear(LinearSpec {
        name: "bbwenet_af3_kernel",
        bias: Some("bbwenet_af3_kernel_bias"),
        subias: Some("bbwenet_af3_kernel_subias"),
        weights: Some("bbwenet_af3_kernel_weights_int8"),
        float_weights: Some("bbwenet_af3_kernel_weights_float"),
        weights_idx: None,
        diag: None,
        scale: Some("bbwenet_af3_kernel_scale"),
        nb_inputs: 128,
        nb_outputs: 48,
    }),
    LayerSpec::Linear(LinearSpec {
        name: "bbwenet_af3_gain",
        bias: Some("bbwenet_af3_gain_bias"),
        subias: None,
        weights: None,
        float_weights: Some("bbwenet_af3_gain_weights_float"),
        weights_idx: None,
        diag: None,
        scale: None,
        nb_inputs: 128,
        nb_outputs: 1,
    }),
];

/// libopus `BBWENETLayers` (`dnn/bbwenet_data.h`): the layers of the model, in
/// the order of the C struct. Each layer borrows its arrays from a weight list.
#[derive(Clone, Copy, Default)]
pub struct BbwenetLayers<'w> {
    /// `bbwenet_fnet_conv1`: float weights, 342 inputs, 128 outputs.
    pub bbwenet_fnet_conv1: LinearLayer<'w>,
    /// `bbwenet_fnet_conv2`: int8 weights, 384 inputs, 128 outputs.
    pub bbwenet_fnet_conv2: LinearLayer<'w>,
    /// `bbwenet_fnet_gru_input`: int8 weights, 128 inputs, 384 outputs.
    pub bbwenet_fnet_gru_input: LinearLayer<'w>,
    /// `bbwenet_fnet_gru_recurrent`: int8 weights, 128 inputs, 384 outputs.
    pub bbwenet_fnet_gru_recurrent: LinearLayer<'w>,
    /// `bbwenet_fnet_tconv`: int8 weights, 128 inputs, 256 outputs.
    pub bbwenet_fnet_tconv: LinearLayer<'w>,
    /// `bbwenet_tdshape1_alpha1_f`: int8 weights, 256 inputs, 80 outputs.
    pub bbwenet_tdshape1_alpha1_f: LinearLayer<'w>,
    /// `bbwenet_tdshape1_alpha1_t`: float weights, 42 inputs, 80 outputs.
    pub bbwenet_tdshape1_alpha1_t: LinearLayer<'w>,
    /// `bbwenet_tdshape1_alpha2`: float weights, 160 inputs, 80 outputs.
    pub bbwenet_tdshape1_alpha2: LinearLayer<'w>,
    /// `bbwenet_tdshape2_alpha1_f`: int8 weights, 256 inputs, 120 outputs.
    pub bbwenet_tdshape2_alpha1_f: LinearLayer<'w>,
    /// `bbwenet_tdshape2_alpha1_t`: float weights, 42 inputs, 120 outputs.
    pub bbwenet_tdshape2_alpha1_t: LinearLayer<'w>,
    /// `bbwenet_tdshape2_alpha2`: float weights, 240 inputs, 120 outputs.
    pub bbwenet_tdshape2_alpha2: LinearLayer<'w>,
    /// `bbwenet_af1_kernel`: int8 weights, 128 inputs, 48 outputs.
    pub bbwenet_af1_kernel: LinearLayer<'w>,
    /// `bbwenet_af1_gain`: float weights, 128 inputs, 3 outputs.
    pub bbwenet_af1_gain: LinearLayer<'w>,
    /// `bbwenet_af2_kernel`: int8 weights, 128 inputs, 288 outputs.
    pub bbwenet_af2_kernel: LinearLayer<'w>,
    /// `bbwenet_af2_gain`: float weights, 128 inputs, 3 outputs.
    pub bbwenet_af2_gain: LinearLayer<'w>,
    /// `bbwenet_af3_kernel`: int8 weights, 128 inputs, 48 outputs.
    pub bbwenet_af3_kernel: LinearLayer<'w>,
    /// `bbwenet_af3_gain`: float weights, 128 inputs, 1 output.
    pub bbwenet_af3_gain: LinearLayer<'w>,
}

impl<'w> BbwenetLayers<'w> {
    /// libopus `init_bbwenetlayers`: builds every layer from the arrays of
    /// `list`, in the order of `LAYERS`. Fails at the first layer whose arrays
    /// are missing, have the wrong size or are malformed; then nothing is
    /// built.
    pub fn init(list: &WeightList<'w>) -> Result<Self, Error> {
        Ok(BbwenetLayers {
            bbwenet_fnet_conv1: LAYERS[0].linear(list)?,
            bbwenet_fnet_conv2: LAYERS[1].linear(list)?,
            bbwenet_fnet_gru_input: LAYERS[2].linear(list)?,
            bbwenet_fnet_gru_recurrent: LAYERS[3].linear(list)?,
            bbwenet_fnet_tconv: LAYERS[4].linear(list)?,
            bbwenet_tdshape1_alpha1_f: LAYERS[5].linear(list)?,
            bbwenet_tdshape1_alpha1_t: LAYERS[6].linear(list)?,
            bbwenet_tdshape1_alpha2: LAYERS[7].linear(list)?,
            bbwenet_tdshape2_alpha1_f: LAYERS[8].linear(list)?,
            bbwenet_tdshape2_alpha1_t: LAYERS[9].linear(list)?,
            bbwenet_tdshape2_alpha2: LAYERS[10].linear(list)?,
            bbwenet_af1_kernel: LAYERS[11].linear(list)?,
            bbwenet_af1_gain: LAYERS[12].linear(list)?,
            bbwenet_af2_kernel: LAYERS[13].linear(list)?,
            bbwenet_af2_gain: LAYERS[14].linear(list)?,
            bbwenet_af3_kernel: LAYERS[15].linear(list)?,
            bbwenet_af3_gain: LAYERS[16].linear(list)?,
        })
    }

    /// Every layer, in the order of `LAYERS`.
    pub fn layers(&self) -> [Layer<'_, 'w>; 17] {
        [
            Layer::Linear(&self.bbwenet_fnet_conv1),
            Layer::Linear(&self.bbwenet_fnet_conv2),
            Layer::Linear(&self.bbwenet_fnet_gru_input),
            Layer::Linear(&self.bbwenet_fnet_gru_recurrent),
            Layer::Linear(&self.bbwenet_fnet_tconv),
            Layer::Linear(&self.bbwenet_tdshape1_alpha1_f),
            Layer::Linear(&self.bbwenet_tdshape1_alpha1_t),
            Layer::Linear(&self.bbwenet_tdshape1_alpha2),
            Layer::Linear(&self.bbwenet_tdshape2_alpha1_f),
            Layer::Linear(&self.bbwenet_tdshape2_alpha1_t),
            Layer::Linear(&self.bbwenet_tdshape2_alpha2),
            Layer::Linear(&self.bbwenet_af1_kernel),
            Layer::Linear(&self.bbwenet_af1_gain),
            Layer::Linear(&self.bbwenet_af2_kernel),
            Layer::Linear(&self.bbwenet_af2_gain),
            Layer::Linear(&self.bbwenet_af3_kernel),
            Layer::Linear(&self.bbwenet_af3_gain),
        ]
    }
}