input: Write a python function to implement the SGD (Stochastic Gradient Descent) optimizer for a simple neural network.
  assert Neural_Network(0.01).sgd([0.5, 1.5, -0.5], 1) == [0.494, 1.494, -0.506]
  assert Neural_Network(0.1).sgd([1, 2, 3], 2) == [0.4, 1.4, 2.4]
  assert Neural_Network(0.05).sgd([0.1, 0.2, 0.3], 0.5) == [0.0925, 0.1925, 0.2925]
output: class Neural_Network:
      def __init__(self, learning_rate):
          self.learning_rate = learning_rate
  
      def sgd(self, weights, gradient):
          updated_weights = [w - self.learning_rate * gradient for w in weights]
          return updated_weights