def __getitem__(self, index):
    '\n        @return\n            `images`: Dim(batch, height, width, channels)\n            `groud_truth_one`:\n                [Dim(batch, yolo.h, yolo.w, yolo.c + len(mask))] * len(yolo)\n        '
    batch_x = []
    batch_y = [[] for _ in range(len(self._metayolos))]
    start_index = (index * self._metanet.batch)
    for i in range((self._metanet.batch - self._augmentation_batch)):
        (image, bboxes) = self._get_dataset((start_index + i))
        self._augmentation_cache[self._augmentation_cache_index] = (image, bboxes)
        self._augmentation_cache_index = ((self._augmentation_cache_index + 1) % _AUGMETATION_CACHE_SIZE)
        batch_x.append(image)
        ground_truth = self._convert_dataset_to_ground_truth(bboxes)
        for j in range(len(self._metayolos)):
            batch_y[j].append(ground_truth[j])
    for i in range(self._augmentation_batch):
        augmentation = self._augmentation[np.random.randint(0, len(self._augmentation))]
        image = None
        bboxes = None
        if (augmentation == 'mosaic'):
            (image, bboxes) = mosaic(*[self._augmentation_cache[np.random.randint(0, _AUGMETATION_CACHE_SIZE)] for _ in range(4)])
        batch_x.append(image)
        ground_truth = self._convert_dataset_to_ground_truth(bboxes)
        for j in range(len(self._metayolos)):
            batch_y[j].append(ground_truth[j])
    return (np.concatenate(batch_x, axis=0), [np.stack(y, axis=0) for y in batch_y])
@return
    `images`: Dim(batch, height, width, channels)
    `groud_truth_one`:
        [Dim(batch, yolo.h, yolo.w, yolo.c + len(mask))] * len(yolo)
def __getitem__(self, index):
    '\n        @return\n            `images`: Dim(batch, height, width, channels)\n            `groud_truth_one`:\n                [Dim(batch, yolo.h, yolo.w, yolo.c + len(mask))] * len(yolo)\n        '
    batch_x = []
    batch_y = [[] for _ in range(len(self._metayolos))]
    start_index = (index * self._metanet.batch)
    for i in range((self._metanet.batch - self._augmentation_batch)):
        (image, bboxes) = self._get_dataset((start_index + i))
        self._augmentation_cache[self._augmentation_cache_index] = (image, bboxes)
        self._augmentation_cache_index = ((self._augmentation_cache_index + 1) % _AUGMETATION_CACHE_SIZE)
        batch_x.append(image)
        ground_truth = self._convert_dataset_to_ground_truth(bboxes)
        for j in range(len(self._metayolos)):
            batch_y[j].append(ground_truth[j])
    for i in range(self._augmentation_batch):
        augmentation = self._augmentation[np.random.randint(0, len(self._augmentation))]
        image = None
        bboxes = None
        if (augmentation == 'mosaic'):
            (image, bboxes) = mosaic(*[self._augmentation_cache[np.random.randint(0, _AUGMETATION_CACHE_SIZE)] for _ in range(4)])
        batch_x.append(image)
        ground_truth = self._convert_dataset_to_ground_truth(bboxes)
        for j in range(len(self._metayolos)):
            batch_y[j].append(ground_truth[j])
    return (np.concatenate(batch_x, axis=0), [np.stack(y, axis=0) for y in batch_y])