input: Write a python function to create and fit a linear regression model using the given features and target arrays.
  assert np.isclose(create_and_fit_model()[0], 0.05384615384615617, atol=1e-6)
  assert np.allclose(create_and_fit_model()[1], [0.30769231, 1.53846154], atol=1e-6)
output: def create_and_fit_model():
      import numpy as np
      from sklearn.linear_model import LinearRegression
  
      # Example data
      X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])
      y = np.dot(X, np.array([1, 2])) + 3
  
      # Create and fit the model
      model = LinearRegression().fit(X, y)
      
      # Obtain the intercept and coefficients
      intercept = model.intercept_
      coefficients = model.coef_
  
      return intercept, coefficients