input: Write a python function to split data into train and test sets, train a linear regression model, and evaluate its performance.
  assert np.isclose(score_model([[1, 2, 3, 4, 5, 6], [1, 2, 3, 4, 5, 6]], [[10, 15, 20, 25, 35, 50], [12, 18, 15, 24, 40, 57]]), 0.99, atol=0.01)
  assert np.isclose(score_model([[1, 2, 3], [4, 5, 6]], [[10, 15, 20], [25, 35, 50]]), 1.0, atol=0.01)
output: from sklearn.model_selection import train_test_split
  from sklearn.linear_model import LinearRegression
  from sklearn.metrics import r2_score
  import numpy as np
  
  def score_model(X, y):
      X_train, X_test, y_train, y_test = train_test_split(np.array(X).T, np.array(y).T, test_size=0.2, random_state=42)
      model = LinearRegression()
      model.fit(X_train, y_train)
      y_pred = model.predict(X_test)
      return r2_score(y_test, y_pred)