def loss_gradient_check(
    estimator: "LossGradientsMixin",
    x: np.ndarray,
    y: np.ndarray,
    training_mode: bool = False,
    verbose: bool = True,
    **kwargs
) -> np.ndarray:
    """
    Compute the gradient of the loss function w.r.t. `x` and identify points where the gradient is zero, nan, or inf

    :param estimator: The classifier to be analyzed.
    :param x: Input with shape as expected by the classifier's model.
    :param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or indices of shape
              (nb_samples,).
    :param training_mode: `True` for model set to training mode and `False` for model set to evaluation mode.
    :param verbose: Show progress bars.
    :return: Array of booleans with the shape (len(x), 3). If true means the gradient of the loss w.r.t. the
             particular `x` was bad (zero, nan, inf).
    """
    assert len(x) == len(y), "x and y must be the same length"

    is_bad = []
    for i in trange(len(x), desc="Gradient check", disable=not verbose):
        grad = estimator.loss_gradient(x=x[[i]], y=y[[i]], training_mode=training_mode, **kwargs)
        is_bad.append(
            [
                (np.min(grad) == 0 and np.max(grad) == 0),
                np.any(np.isnan(grad)),
                np.any(np.isinf(grad)),
            ]
        )

    return np.array(is_bad, dtype=bool)

    Compute the gradient of the loss function w.r.t. `x` and identify points where the gradient is zero, nan, or inf

    :param estimator: The classifier to be analyzed.
    :param x: Input with shape as expected by the classifier's model.
    :param y: Target values (class labels) one-hot-encoded of shape (nb_samples, nb_classes) or indices of shape
              (nb_samples,).
    :param training_mode: `True` for model set to training mode and `False` for model set to evaluation mode.
    :param verbose: Show progress bars.
    :return: Array of booleans with the shape (len(x), 3). If true means the gradient of the loss w.r.t. the
             particular `x` was bad (zero, nan, inf).
    
art/metrics/gradient_check.py