hpt 0.1.3

High Performance Tensor (HPT) - A fast, efficient, and user-friendly tensor computation library for Rust
Documentation 
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use hpt_common::{error::base::TensorError, shape::shape::Shape};
use hpt_types::{dtype::TypeCommon, into_scalar::Cast, type_promote::FloatOutBinary};

use crate::backend::Cpu;
use crate::ops::FloatOutPooling;
use crate::ops::NormalPooling;
use crate::Tensor;
use hpt_allocator::traits::{Allocator, AllocatorOutputRetrive};
use hpt_traits::tensor::CommonBounds;

impl<T, const DEVICE: usize, A> FloatOutPooling for Tensor<T, Cpu, DEVICE, A>
where
    T: CommonBounds
        + FloatOutBinary<<T as FloatOutBinary>::Output, Output = <T as FloatOutBinary>::Output>,
    <T as FloatOutBinary>::Output: CommonBounds,
    T::Vec: FloatOutBinary<
        <<T as FloatOutBinary>::Output as TypeCommon>::Vec,
        Output = <<T as FloatOutBinary>::Output as TypeCommon>::Vec,
    >,
    bool: Cast<T>,
    i64: Cast<<T as FloatOutBinary>::Output>,
    A: Allocator + Send + Sync,
    A::Output: AllocatorOutputRetrive,
{
    type Output = Tensor<<T as FloatOutBinary>::Output, Cpu, DEVICE, A>;
    #[track_caller]
    fn avgpool2d<S: Into<Shape>>(
        &self,
        kernels_shape: S,
        steps: [i64; 2],
        padding: [(i64, i64); 2],
        dilation: [i64; 2],
    ) -> Result<Self::Output, TensorError> {
        Ok(self
            .inner
            .avgpool2d(kernels_shape, steps, padding, dilation)?
            .into())
    }

    #[track_caller]
    fn adaptive_avgpool2d(&self, output_size: [i64; 2]) -> Result<Self::Output, TensorError> {
        Ok(self.inner.adaptive_avgpool2d(output_size)?.into())
    }
}

impl<T, const DEVICE: usize, A> NormalPooling for Tensor<T, Cpu, DEVICE, A>
where
    T: CommonBounds,
    bool: Cast<T>,
    i64: Cast<T>,
    A: Allocator + Send + Sync,
    A::Output: AllocatorOutputRetrive,
{
    type Output = Tensor<T, Cpu, DEVICE, A>;
    #[track_caller]
    fn maxpool2d<S: Into<Shape>>(
        &self,
        kernels_shape: S,
        steps: [i64; 2],
        padding: [(i64, i64); 2],
        dilation: [i64; 2],
    ) -> Result<Self::Output, TensorError> {
        Ok(self
            .inner
            .maxpool2d(kernels_shape, steps, padding, dilation)?
            .into())
    }

    #[track_caller]
    fn adaptive_maxpool2d(&self, output_size: [i64; 2]) -> Result<Self::Output, TensorError> {
        Ok(self.inner.adaptive_maxpool2d(output_size)?.into())
    }
}