def median_absolute_error(y_true, y_pred):
    """Median absolute error regression loss

    Read more in the :ref:`User Guide <median_absolute_error>`.

    Parameters
    ----------
    y_true : array-like of shape = (n_samples)
        Ground truth (correct) target values.

    y_pred : array-like of shape = (n_samples)
        Estimated target values.

    Returns
    -------
    loss : float
        A positive floating point value (the best value is 0.0).

    Examples
    --------
    >>> from sklearn.metrics import median_absolute_error
    >>> y_true = [3, -0.5, 2, 7]
    >>> y_pred = [2.5, 0.0, 2, 8]
    >>> median_absolute_error(y_true, y_pred)
    0.5

    """
    y_type, y_true, y_pred, _ = _check_reg_targets(y_true, y_pred,
                                                   'uniform_average')
    if y_type not in 'continuous-multioutput':
        raise ValueError("Multioutput not supported in median_absolute_error")
    return np.median(np.abs(y_pred - y_true))
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/metrics/regression.py/median_absolute_error/operator `==` -> `not in`