def euclidean_distances(X, Y=None, Y_norm_squared=None, squared=False,
                        X_norm_squared=None):
    X, Y = check_pairwise_arrays(X, Y)

    if X_norm_squared is not None:
        XX = check_array(X_norm_squared)
        if XX.shape == (1, X.shape[0]):
            XX = XX.T
        elif XX.shape != (X.shape[0], 1):
            raise ValueError(
                "Incompatible dimensions for X and X_norm_squared")
    else:
        XX = row_norms(X, squared=True)[:, np.newaxis]

    if X is Y:  # shortcut in the common case euclidean_distances(X, X)
        YY = XX.T
    elif Y_norm_squared is not None:
        YY = np.atleast_2d(Y_norm_squared)

        if YY.shape != (1, Y.shape[0]):
            raise ValueError(
                "Incompatible dimensions for Y and Y_norm_squared")
    else:
        YY = row_norms(Y, squared=True)[np.newaxis, :]

    distances = safe_sparse_dot(X, Y.T, dense_output=True)
    distances *= -2
    distances += XX
    distances += YY
    np.maximum(distances, 0, out=distances)

    if X is Y:
        # Ensure that distances between vectors and themselves are set to 0.0.
        # This may not be the case due to floating point rounding errors.
        distances.flat[::distances.shape[0] + 1] = 0.0

    return distances if squared else np.sqrt(distances, out=distances)
Args:
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/metrics/pairwise.py/euclidean_distances--crossover with--dataset/ETHPy150Open dddomodossola/remi/remi/gui.py/GenericObject.__init__