input: Write a python function to implement Stochastic Gradient Descent for generating a linear regression model.
  assert sgd_model(np.array([1, 2, 3]), np.array([1, 2, 3]), 0, 0, 0.01, 1000) == (1.0, 0.0)
  assert sgd_model(np.array([1, 2, 3]), np.array([2, 4, 6]), 0, 0, 0.01, 1000) == (2.0, 0.0)
  assert sgd_model(np.array([1, 2, 3, 4]), np.array([2, 4, 6, 8]), 0, 0, 0.01, 1000) == (2.0, 0.0)
output: import numpy as np
  
  def sgd_model(X, y, w, b, learning_rate, epochs):
      n = len(X)
      
      for _ in range(epochs):
          for i in range(n):
              xi = X[i]
              yi = y[i]
              prediction = w * xi + b
              error = prediction - yi
              w_gradient = 2 * xi * error
              b_gradient = 2 * error
              w -= learning_rate * w_gradient
              b -= learning_rate * b_gradient
              
      return w, b