def METHOD_NAME(self, x: np.ndarray) -> np.ndarray:
    """
    Perform prediction of the given classifier for a batch of inputs, potentially filtering suspicious input
    :param x: Input samples.
    :return: Array of predictions of shape `(nb_inputs, nb_classes)`.
    """
    raw_predictions = self.predict_fn(x)
    if self.entropy_threshold is None or self.validation_data is None:  # pragma: no cover
        logger.warning("Mitigation has not been performed. Predictions may be unsafe.")
        return raw_predictions
    x_val = self.validation_data
    final_predictions = []
    for i, img in enumerate(x):
        # Randomly select samples from test set
        selected_indices = np.random.choice(np.arange(len(x_val)), self.num_samples)
        # Perturb the images by combining them
        perturbed_images = np.array([combine_images(img, x_val[idx]) for idx in selected_indices])
        # Predict on the perturbed images
        perturbed_predictions = self.predict_fn(perturbed_images)
        # Calculate normalized entropy
        normalized_entropy = np.sum(entropy(perturbed_predictions, base=2, axis=0)) / float(self.num_samples)
        # Abstain if entropy is below threshold
        if normalized_entropy <= self.entropy_threshold:
            final_predictions.append(self.abstain())
        else:
            final_predictions.append(raw_predictions[i])
    return np.array(final_predictions)