code: def update_parameters(parameters, grads, learning_rate=1.2):
      
      # Retrieve each parameter from the dictionary "parameters"
      W1 = parameters["W1"]
      b1 = parameters["b1"]
      W2 = parameters["W2"]
      b2 = parameters["b2"]
  
      # Retrieve each gradient from the dictionary "grads"
      dW1 = grads["dW1"]
      db1 = grads["db1"]
      dW2 = grads["dW2"]
      db2 = grads["db2"]
  
      # Update rule for each parameter
      W1 -= learning_rate * dW1
      b1 -= learning_rate * db1
      W2 -= learning_rate * dW2
      b2 -= learning_rate * db2
  
      parameters = {"W1": W1,
                    "b1": b1,
                    "W2": W2,
                    "b2": b2}
  
      return parameters
docs: Updates parameters using the gradient descent update rule given above
  
      Arguments:
      parameters -- python dictionary containing your parameters
      grads -- python dictionary containing your gradients
  
      Returns:
      parameters -- python dictionary containing your updated parameters