input: Write a function that implements the Gradient Descent Algorithm to find the optimal parameters for a linear regression model. The function should take input data `x` and `y`, a learning rate, and the number of iterations as parameters. It should update the parameters in each iteration and return the final values of the slope `m_curr` and intercept `b_curr`.
  
  def gradient_descent(x, y, learning_rate=0.01, iterations=100):
      m_curr = b_curr = 0
      n = len(x)
      for i in range(iterations):
          y_predicted = m_curr * x + b_curr
          cost = (1/n) * sum([val**2 for val in (y-y_predicted)])
          md = -(2/n)*sum(x*(y-y_predicted))
          bd = -(2/n)*sum(y-y_predicted)
          m_curr = m_curr - learning_rate * md
          b_curr = b_curr - learning_rate * bd
          print ("m {}, b {}, cost {} iteration {}".format(m_curr,b_curr,cost, i))
      return m_curr, b_curr
  
  assert gradient_descent(np.array([1, 2, 3, 4, 5]), np.array([5, 7, 9, 11, 13])) == (2.0, 3.0)
  assert gradient_descent(np.array([1, 2, 3]), np.array([3, 4, 5])) == (1.0, 2.0)
  assert gradient_descent(np.array([]), np.array([])) == (0.0, 0.0)
output: def gradient_descent(x, y, learning_rate=0.01, iterations=100):
      m_curr = b_curr = 0
      n = len(x)
      for i in range(iterations):
          y_predicted = m_curr * x + b_curr
          cost = (1/n) * sum([val**2 for val in (y-y_predicted)])
          md = -(2/n)*sum(x*(y-y_predicted))
          bd = -(2/n)*sum(y-y_predicted)
          m_curr = m_curr - learning_rate * md
          b_curr = b_curr - learning_rate * bd
      return m_curr, b_curr