PyTorch eager

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    A model that performs a convolution transpose, minimum operation, sum operation, GELU activation and addition.
    """
    def __init__(self, in_channels, out_channels, kernel_size, stride, padding, output_padding, bias_shape):
        super(Model, self).__init__()
        self.conv_transpose = nn.ConvTranspose2d(in_channels, out_channels, kernel_size, stride, padding, output_padding)
        self.bias = nn.Parameter(torch.randn(bias_shape))

    def forward(self, x):
        x = self.conv_transpose(x)
        x = torch.min(x, dim=1, keepdim=True)[0]  # Minimum operation along channel dimension
        x = torch.sum(x, dim=2, keepdim=True)  # Sum operation along height dimension
        x = torch.nn.functional.gelu(x)  # GELU activation
        x = x + self.bias
        return x

batch_size = 16
in_channels = 64
out_channels = 128
height, width = 128, 128
kernel_size = 3
stride = 2
padding = 1
output_padding = 1
bias_shape = (1, 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, stride, padding, output_padding, bias_shape]