human
Solve the following python programming problem given the following code:
# Network parameters
hidden_encoder_1 = 500
hidden_encoder_2 = 250
hidden_encoder_3 = 100
hidden_encoder = [hidden_encoder_1, hidden_encoder_2, hidden_encoder_3]

hidden_decoder_1 = 100
hidden_decoder_2 = 250
hidden_decoder_3 = 500
hidden_decoder = [hidden_decoder_1, hidden_decoder_2, hidden_decoder_3]

class Autoencoder(nn.Module):
    def __init__(self, hidden_encoder, hidden_decoder, image_size, channel_uses):
        super(Autoencoder, self).__init__()
        # Define Transmitter Layer: Linear function, M input neurons (symbols), 2 output neurons (real and imaginary part)        
        self.We1 = nn.Linear(image_size*image_size, hidden_encoder[0]) 
        self.We2 = nn.Linear(hidden_encoder[0], hidden_encoder[1]) 
        self.We3 = nn.Linear(hidden_encoder[1], hidden_encoder[2]) 
        self.We4 = nn.Linear(hidden_encoder[2], 2*channel_uses) 
        
        # Define Receiver Layer: Linear function, 2 input neurons (real and imaginary part), M output neurons (symbols)
        self.Wd1 = nn.Linear(2*channel_uses,hidden_decoder[0]) 
        self.Wd2 = nn.Linear(hidden_decoder[0], hidden_decoder[1]) 
        self.Wd3 = nn.Linear(hidden_decoder[1], hidden_decoder[2]) 
        self.Wd4 = nn.Linear(hidden_decoder[2], image_size*image_size) 

        # Non-linearity (used in transmitter and receiver)
        self.activation_function = nn.ELU()    
        self.sigmoid = nn.Sigmoid()

    def forward(self, training_data, sigma_n):
        compressed = self.encoder(training_data)
        transmitted = self.channel(compressed, sigma_n)
        reconstructed = self.decoder(transmitted)
        return reconstructed
        
    def encoder(self, batch):
        temp = self.activation_function(self.We1(batch))
        temp = self.activation_function(self.We2(temp))
        temp = self.activation_function(self.We3(temp))
        output = self.activation_function(self.We4(temp))
        return output
    
    def decoder(self, batch):
        temp = self.activation_function(self.Wd1(batch))
        temp =...
Given the above code, generate some markdown instructions for it.
assistant
#Here is the Python program you requested.

Specify neural network
Transmitter and receiver consist of each 3 hidden layers with ELU activation function