input: Write a Python function to fit a linear regression model and make predictions.
  assert (LinearRegression().fit([[1, 2], [2, 3], [3, 4]], [2, 3, 4]).predict([[4, 5]]) == np.array([5.])).all()
  assert (LinearRegression().fit([[1], [2], [3]], [1, 2, 3]).predict([[4]]) == np.array([4.])).all()
  assert (LinearRegression().fit([[0, 0], [1, 1]], [0, 1]).predict([[2, 2]]) == np.array([2.])).all()
output: def LinearRegression():
      from sklearn.linear_model import LinearRegression as LR
      import numpy as np
  
      class CustomLinearRegression:
          def __init__(self):
              self.model = LR()
  
          def fit(self, X, y):
              self.model.fit(X, y)
              return self
  
          def predict(self, X):
              return self.model.predict(X)
  
      return CustomLinearRegression()