import numpy as np
from sklearn.metrics import mean_squared_error as skmean_squared_error

def mean_squared_error(y_true, y_pred):
    return np.mean((y_true - y_pred) ** 2)

for i in range(10):
    rng = np.random.RandomState(i)
    y_true = rng.rand(10)
    y_pred = rng.rand(10)
    score1 = mean_squared_error(y_true, y_pred)
    score2 = skmean_squared_error(y_true, y_pred)
    assert np.isclose(score1, score2)