human
I am working on the file "DEEP LEARNING/Pytorch from scratch/TODO/GAN/cycle-gan/CycleGAN_Exercise.ipynb".
The first task is:
Define the Discriminator Architecture
Your task is to fill in the __init__ function with the specified 5 layer conv net architecture. Both $D_X$ and $D_Y$ have the same architecture, so we only need to define one class, and later instantiate two discriminators. 

It's recommended that you use a kernel size of 4x4 and use that to determine the correct stride and padding size for each layer. This Stanford resource may also help in determining stride and padding sizes.

Define your convolutional layers in __init__
Then fill in the forward behavior of the network

The forward function defines how an input image moves through the discriminator, and the most important thing is to pass it through your convolutional layers in order, with a ReLu activation function applied to all but the last layer.
You should not apply a sigmoid activation function to the output, here, and that is because we are planning on using a squared error loss for training. And you can read more about this loss function, later in the notebook.
Can you write Python program for it?
 Give me a Python program to determine the correct stride and padding size for each layer?
assistant
#Here is Python program to determine the correct stride and padding size for each layer.

class Discriminator(nn.Module):
    
    def __init__(self, conv_dim=64):
        super(Discriminator, self).__init__()

        # Define all convolutional layers
        # Should accept an RGB image as input and output a single value
        

    def forward(self, x):
        # define feedforward behavior
        
        return x