PyTorch eager

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Simple model that performs a convolution, applies Mish, and another Mish.
    """
    def __init__(self, in_channels, out_channels, kernel_size):
        super(Model, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)

    def forward(self, x):
        x = self.conv(x)
        x = torch.nn.functional.mish(x)
        x = torch.nn.functional.mish(x)
        return x

batch_size   = 64  
in_channels  = 64  
out_channels = 128  
height = width = 256
kernel_size = 3

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

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