human
I am working on the file "Seminar3/HW3_Modules.ipynb".
The first task is:
Module is an abstract class which defines fundamental methods necessary for a training a neural network. You do not need to change anything here, just read the comments.
Can you write Python code for it?
 Write me a the Python code you requested?
assistant
#Here is the Python code you requested.

class Module(object):
    def __init__ (self):
        self.output = None
        self.gradInput = None
        self.training = True
    """
    Basically, you can think of a module as of a something (black box) 
    which can process `input` data and produce `ouput` data.
    This is like applying a function which is called `forward`: 
        
        output = module.forward(input)
    
    The module should be able to perform a backward pass: to differentiate the `forward` function. 
    More, it should be able to differentiate it if is a part of chain (chain rule).
    The latter implies there is a gradient from previous step of a chain rule. 
    
        gradInput = module.backward(input, gradOutput)
    """
    
    def forward(self, input):
        """
        Takes an input object, and computes the corresponding output of the module.
        """
        return self.updateOutput(input)

    def backward(self,input, gradOutput):
        """
        Performs a backpropagation step through the module, with respect to the given input.
        
        This includes 
         - computing a gradient w.r.t. `input` (is needed for further backprop),
         - computing a gradient w.r.t. parameters (to update parameters while optimizing).
        """
        self.updateGradInput(input, gradOutput)
        self.accGradParameters(input, gradOutput)
        return self.gradInput
    

    def updateOutput(self, input):
        """
        Computes the output using the current parameter set of the class and input.
        This function returns the result which is stored in the `output` field.
        
        Make sure to both store the data in `output` field and return it. 
        """
        
        # The easiest case:
            
        # self.output = input 
        # return self.output
        
        pass

    def updateGradInput(self, input, gradOutput):
        """
        Computing the gradient of the module with respect to its own input. 
        This is returned in `gradInput`. Also, the...