input: Write a function to implement the Gradient Descent Algorithm for optimizing a linear regression model. The function should take in the input data x and y, along with optional parameters for the learning rate and number of iterations. It should return the optimized slopes m and b.
  
  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], [1, 2, 4]) == (0.9, 0.2)
  assert gradient_descent([1, 3, 5], [2, 6, 10]) == (2.0, 0.0)
  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