input: Write a function to implement the Gradient Descent Algorithm that iteratively adjusts the slope and intercept of a linear regression model to minimize the cost function. The function should take in the input data x and y, a learning rate, and the number of iterations as parameters. If the number of iterations is reached without converging, it should return the final slope and intercept.
  
  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
  
  x = np.array([1, 2, 3, 4, 5])
  y = np.array([5, 7, 9, 11, 13])
  assert gradient_descent(x, y) == (2.0, 3.0)
  assert gradient_descent(np.array([1, 2]), np.array([3, 4])) == (1.0, 2.0)
  assert gradient_descent(np.array([1]), np.array([2])) == (1.0, 1.0)
  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