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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def _count_classified_pixels(self):
 """ Count the pixels belonging to each classified class. """

        class_values = self.class_dictionary.values()

        classification_count = np.array([[[np.count_nonzero(prediction[np.nonzero(mask)] == class_val)
                                           for prediction, mask in zip(self.classification_masks, masktype)]
                                          for masktype in self.truth_masks]
                                         for class_val in class_values])

        classification_count = np.moveaxis(classification_count, 0, -1)
        classification_count = np.moveaxis(classification_count, 0, -2)

        if self.pixel_classification_counts is None:
            self.pixel_classification_counts = np.copy(classification_count)
        else:
            self.pixel_classification_counts = np.concatenate((self.pixel_classification_counts, classification_count))