Does .fit() method equal to gradient descent function?

Does method .fit() from Linear Regression Model equal to gradient descent function what we do in lesson here:

def get_gradient_at_b(x, y, b, m):
  N = len(x)
  diff = 0
  for i in range(N):
    x_val = x[i]
    y_val = y[i]
    diff += (y_val - ((m * x_val) + b))
  b_gradient = -(2/N) * diff  
  return b_gradient

def get_gradient_at_m(x, y, b, m):
  N = len(x)
  diff = 0
  for i in range(N):
      x_val = x[i]
      y_val = y[i]
      diff += x_val * (y_val - ((m * x_val) + b))
  m_gradient = -(2/N) * diff  
  return m_gradient

#Your step_gradient function here
def step_gradient(b_current, m_current, x, y, learning_rate):
    b_gradient = get_gradient_at_b(x, y, b_current, m_current)
    m_gradient = get_gradient_at_m(x, y, b_current, m_current)
    b = b_current - (learning_rate * b_gradient)
    m = m_current - (learning_rate * m_gradient)
    return [b, m]

def gradient_descent(x, y, learning_rate, num_iterations):
    b = 0
    m = 0
    for i in range(num_iterations):
        b, m = step_gradient(b, m, x, y, learning_rate)
    return [b, m]

I mean does .fit() returns out m, b coefficient ?