PyTorch eager

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Model that performs a transposed convolution, batch normalization, tanh activation, max pooling, and group normalization.
    """
    def __init__(self, in_channels, out_channels, kernel_size, stride, padding, groups, num_groups):
        super(Model, self).__init__()
        self.conv_transpose = nn.ConvTranspose2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding)
        self.batch_norm = nn.BatchNorm2d(out_channels)
        self.tanh = nn.Tanh()
        self.max_pool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.group_norm = nn.GroupNorm(num_groups=num_groups, num_channels=out_channels)

    def forward(self, x):
        x = self.conv_transpose(x)
        x = self.batch_norm(x)
        x = self.tanh(x)
        x = self.max_pool(x)
        x = self.group_norm(x)
        return x

batch_size = 512
in_channels  = 64  
out_channels = 128  
height = width = 2048  
kernel_size  = 5
stride       = 1  
padding      = 1
groups       = 8
num_groups   = 8
height, width = 32, 32

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

def get_init_inputs():
    return [in_channels, out_channels, kernel_size, stride, padding, groups, num_groups]