Extracting hidden features from Autoencoders using Pytorch

Following the tutorials in this post, I am trying to train an autoencoder and extract the features from its hidden layer.

So here are my questions:

1. In the autoencoder class, there is a "forward" function. However, I cannot see anywhere in the code that this function is called. So how does it get trained?

2. My question above is because I feel if I want to extract the features, I should add another function (f"orward_hidden") in the autoencoder class:

    def forward(self, features):
        #print("in forward")
        #print(type(features))
        activation = self.encoder_hidden_layer(features)
        activation = torch.relu(activation)
        code = self.encoder_output_layer(activation)
        code = torch.relu(code)
        activation = self.decoder_hidden_layer(code)
        activation = torch.relu(activation)
        activation = self.decoder_output_layer(activation)
        reconstructed = torch.relu(activation)
        return reconstructed
   
    def forward_hidden(self, features):
        activation = self.encoder_hidden_layer(features)
        activation = torch.relu(activation)
        code = self.encoder_output_layer(activation)
        code = torch.relu(code)
        return code
   

Then, after training, which means after this line in the main code:

print("AE, epoch : {}/{}, loss = {:.6f}".format(epoch + 1, epochs_AE, loss))

I can put the following code to retrieve the features from the hidden layer:

hidden_features = model_AE.forward_hidden(my_input)

Is this way correct? Still, I am wondering how the "forward" function was used for training. Because I cannot see it anywhere in the code that is being called.