PyTorch eager

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Performs a depthwise 2D convolution with asymmetric input and asymmetric kernel.

    Args:
        in_channels (int): Number of channels in the input tensor.
        out_channels (int): Number of channels produced by the convolution.
        kernel_size_h (int): Height of the convolution kernel.
        kernel_size_w (int): Width of the convolution kernel.
        stride_h (int, optional): Stride of the convolution in height dimension. Defaults to 1.
        stride_w (int, optional): Stride of the convolution in width dimension. Defaults to 1.
        padding_h (int, optional): Padding applied to the input in height dimension. Defaults to 0.
        padding_w (int, optional): Padding applied to the input in width dimension. Defaults to 0.
        dilation_h (int, optional): Spacing between kernel elements in height dimension. Defaults to 1.
        dilation_w (int, optional): Spacing between kernel elements in width dimension. Defaults to 1.
        groups (int, optional): Number of blocked connections from input channels to output channels. Defaults to 1.
        bias (bool, optional): If `True`, adds a learnable bias to the output. Defaults to `False`.
    """
    def __init__(self, in_channels: int, out_channels: int, kernel_size_h: int, kernel_size_w: int, stride_h: int = 1, stride_w: int = 1, padding_h: int = 0, padding_w: int = 0, dilation_h: int = 1, dilation_w: int = 1, groups: int = 1, bias: bool = False):
        super(Model, self).__init__()
        self.conv2d = nn.Conv2d(in_channels, in_channels, (kernel_size_h, kernel_size_w), stride=(stride_h, stride_w), padding=(padding_h, padding_w), dilation=(dilation_h, dilation_w), groups=in_channels, bias=bias)
        
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Performs the depthwise 2D convolution.

        Args:
            x (torch.Tensor): Input tensor of shape (batch_size, in_channels, height, width).

        Returns:
            torch.Tensor: Output tensor of shape (batch_size, out_channels, height_out, width_out).
        """
        return self.conv2d(x)

# Test code
batch_size = 32
in_channels = 128
out_channels = 128
kernel_size_h = 3
kernel_size_w = 7
width = 256
height = 128
stride_h = 1
stride_w = 1
padding_h = 0
padding_w = 0
dilation_h = 1
dilation_w = 1
groups = in_channels

def get_inputs():
    x = torch.rand(batch_size, in_channels, height, width)
    return [x]

def get_init_inputs():
    return [in_channels, out_channels, kernel_size_h, kernel_size_w, stride_h, stride_w, padding_h, padding_w, dilation_h, dilation_w, groups]