input: Write a function to implement gradient descent algorithm in Python. The function should take inputs x, y, learning_rate, and iterations and return the final values of m and b after the specified number of iterations.
  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([1, 2, 3], [1, 2, 3])==(1.0, 0.0)
  assert gradient_descent([1, 2, 3], [2, 4, 6])==(2.0, 0.0)
  assert gradient_descent([1, 2, 3], [3, 5, 7])==(2.0, 1.0)
  assert gradient_descent([1, 2, 3], [2, 4, 7])==(2.33, 0.33)
  assert gradient_descent([], [])==(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