class_docstr: Represents dataset batch returned by the dataloader during iteration.
class_docstr_tok: ["Represents","dataset","batch","returned","by","the","dataloader","during","iteration","."]
code: def __init__(
      self,
      batch: BatchOutputFormat,
      task_type: TaskType,
      images_seen_num: int,
  ):
      self._task_type = task_type
      self._batch = batch
      self._labels, self._predictions, self._images = None, None, None
      (
          self._embeddings,
          self._additional_data,
      ) = (
          None,
          None,
      )
      self._image_identifiers = batch.get("image_identifiers")
      # if there are no image identifiers, use the number of the image in loading process as identifier
      if self._image_identifiers is None:
          self._image_identifiers = np.asarray(
              range(images_seen_num, images_seen_num + len(self)),
              dtype="str",
          )
  
      self._vision_properties_cache = dict.fromkeys(PropertiesInputType)
  
imports: from typing import Dict, List, Optional, Union
  import numpy
  from deepchecks.core.errors import DeepchecksProcessError
  from deepchecks.vision.utils.image_functions import crop_image
  from deepchecks.vision.utils.image_properties import calc_default_image_properties, default_image_properties
  from deepchecks.vision.utils.vision_properties import PropertiesInputType, calc_vision_properties, validate_properties
  from deepchecks.vision.vision_data.utils import BatchOutputFormat, TaskType, sequence_to_numpy
label_desc: Use image node_id 1 to create a new BatchWrapper object  with example: obj = BatchWrapper(batch, task_type, images_seen_num)