def _average_path_length(n_samples_leaf):
    """
    The average path length in a n_samples iTree, which is equal to
    the average path length of an unsuccessful BST search since the
    latter has the same structure as an isolation tree.
    Parameters
    ----------
    n_samples_leaf : array-like of shape (n_samples,)
        The number of training samples in each test sample leaf, for
        each estimators.

    Returns
    -------
    average_path_length : ndarray of shape (n_samples,)
    """

    n_samples_leaf = check_array(n_samples_leaf, ensure_2d=False)

    n_samples_leaf_shape = n_samples_leaf.shape
    n_samples_leaf = n_samples_leaf.reshape((1, -1))
    average_path_length = np.zeros(n_samples_leaf.shape)

    mask_1 = n_samples_leaf <= 1
    mask_2 = n_samples_leaf == 2
    not_mask = ~np.logical_or(mask_1, mask_2)

    average_path_length[mask_1] = 0.
    average_path_length[mask_2] = 1.
    average_path_length[not_mask] = (
            2.0 * (np.log(n_samples_leaf[not_mask] - 1.0) + np.euler_gamma)
            - 2.0 * (n_samples_leaf[not_mask] - 1.0) / n_samples_leaf[not_mask]
    )

    return average_path_length.reshape(n_samples_leaf_shape)

    The average path length in a n_samples iTree, which is equal to
    the average path length of an unsuccessful BST search since the
    latter has the same structure as an isolation tree.
    Parameters
    ----------
    n_samples_leaf : array-like of shape (n_samples,)
        The number of training samples in each test sample leaf, for
        each estimators.

    Returns
    -------
    average_path_length : ndarray of shape (n_samples,)
    
pyod/models/dif.py