PyTorch eager

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Performs a standard 3D convolution operation with a square input and an 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 (tuple): Size of the convolution kernel (kernel_width, kernel_height, kernel_depth).
        stride (int, optional): Stride of the convolution. Defaults to 1.
        padding (int or tuple, optional): Padding applied to the input. Defaults to 0.
        dilation (int or tuple, optional): Spacing between kernel elements. 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: tuple, stride: int = 1, padding: int = 0, dilation: int = 1, groups: int = 1, bias: bool = False):
        super(Model, self).__init__()
        self.conv3d = nn.Conv3d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias)
        
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Performs the 3D convolution.

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

        Returns:
            torch.Tensor: Output tensor of shape (batch_size, out_channels, width_out, height_out, depth_out).
        """
        return self.conv3d(x)

# Test code
batch_size = 16
in_channels = 3
out_channels = 64
kernel_size = (3, 5, 7)  # Asymmetric kernel
width = 64
height = 64
depth = 64

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

def get_init_inputs():
    return [in_channels, out_channels, kernel_size]  # Provide in_channels, out_channels, kernel_size for initialization