PyTorch eager

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Model that performs a transposed convolution, subtracts a bias term, and applies tanh activation.
    """
    def __init__(self, in_channels, out_channels, kernel_size, bias_shape, stride=2, padding=1, output_padding=1):
        super(Model, self).__init__()
        self.conv_transpose = nn.ConvTranspose2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, output_padding=output_padding)
        self.bias = nn.Parameter(torch.randn(bias_shape)) 

    def forward(self, x):
        x = self.conv_transpose(x)
        x = x - self.bias
        x = torch.tanh(x)
        return x

batch_size = 32
in_channels  = 64  
out_channels = 64  
height = width = 256 
kernel_size = 4
bias_shape = (out_channels, 1, 1)

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

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