PyTorch eager

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Model that performs a 3D convolution, applies Mish activation, and then applies Tanh activation.
    """
    def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0):
        super(Model, self).__init__()
        self.conv = nn.Conv3d(in_channels, out_channels, kernel_size, stride=stride, padding=padding)

    def forward(self, x):
        """
        Args:
            x (torch.Tensor): Input tensor of shape (batch_size, in_channels, D, H, W).

        Returns:
            torch.Tensor: Output tensor of shape (batch_size, out_channels, D', H', W').
        """
        x = self.conv(x)
        x = torch.nn.functional.mish(x)
        x = torch.tanh(x)
        return x

batch_size = 16
in_channels = 32
out_channels = 64
D, H, W = 32, 64, 64
kernel_size = 3

def get_inputs():
    return [torch.rand(batch_size, in_channels, D, H, W)]

def get_init_inputs():
    return [in_channels, out_channels, kernel_size]