PyTorch eager

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Simple model that performs a convolution, applies Instance Normalization, and divides by a constant.
    """
    def __init__(self, in_channels, out_channels, kernel_size, divide_by):
        super(Model, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)
        self.instance_norm = nn.InstanceNorm2d(out_channels)
        self.divide_by = divide_by

    def forward(self, x):
        x = self.conv(x)
        x = self.instance_norm(x)
        x = x / self.divide_by
        return x

batch_size = 128
in_channels  = 64  
out_channels = 128  
height = width = 128  
kernel_size = 3
divide_by = 2.0

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

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