Implement the Neuron class, which will have several methods: __init__. Takes as input an array of neuron weights --- =(1,…,)w=(w 1​ ,…,w n​ ), as well as the activation function f (default argument (�) = � f(x)=x). Stores the weights and the function inside the class. forward. Takes an array as input � = ( � 1 , … , � � ) x=(x 1 ​ ,…,x n ​ ) --- neuron inputs. Returns the value of the function � f applied to a number � 1 � 1 + … + � � � � w 1 ​ x 1 ​ +…+w n ​ x n ​ . backlog. Returns an array � x, which was fed into the input of the forward function the last time it was called. Returns None if forward has not been called before. You won't need the print function for this assignment. The results of the execution of functions should be returned, not printed! forward. Takes an array as input � = ( � 1 , … , � � ) x=(x 1 ​ ,…,x n ​ ) --- neuron inputs. Returns the value of the function � f applied to a number � 1 � 1 + … + � � � � w 1 ​ x 1 ​ +…+w n ​ x n ​ . backlog. Returns an array � x, which was fed into the input of the forward function the last time it was called. Returns None if forward has not been called before. You won't need the print function for this assignment. The results of the execution of functions should be returned, not printed!

class Neuron:
    def __init__(self, weights, activation_function = lambda x: x):
        weights = []
        self.weights = weights
        self.activation_function = activation_function
        self.x = None
        
    def forward(self, input_data):
        self.x = input_data
        y = 0
        for w_i, x_i in zip(self.weights, input_data):
            y += w_i * x_i
        return self.activation_function(y)

    def backward(self):
        return self.x