class_docstr: VisionData is the base data object of deepcheck vision used as input to checks and suites.
  
  Parameters
  ----------
  batch_loader :
      A batch_loader which load a batch of data in an iterative manner. Batch loader batch output must be a
      dictionary in BatchOutputFormat format. The batch loader must provide SHUFFLED batches.
  task_type : str
      The task type of the data. can be one of the following: 'classification', 'semantic_segmentation',
      'object_detection', 'other'. For 'other', only image related checks (such as ImagePropertyOutliers) will be run.
  label_map : Dict[int, str], optional
      A dictionary mapping class ids to their names.
  dataset_name: str, optional
      Name of the dataset to use in the displays instead of "Train" or "Test".
  reshuffle_data: bool, default=True
      If True we will attempt to shuffle the batch loader. Only set this to False if the data is already shuffled.
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docstr: Return True if the batch loader contains additional_data.
docstr_tok: ["Return","True","if","the","batch","loader","contains","additional_data","."]
code: def has_additional_data(self) -> bool:
      """Return True if the batch loader contains additional_data."""
      return self._has_additional_data
  
imports: import sys
  import typing
  from collections import defaultdict
  import numpy
  from typing_extensions import Literal
  from deepchecks.core.errors import DeepchecksValueError, ValidationError
  from deepchecks.core.serialization.html_display import HtmlDisplayableResult
  from deepchecks.utils.ipython import is_notebook, is_sphinx
  from deepchecks.vision.utils.detection_formatters import DEFAULT_PREDICTION_FORMAT
  from deepchecks.vision.utils.image_functions import draw_bboxes, draw_masks, prepare_thumbnail, random_color_dict
  from deepchecks.vision.vision_data import TaskType
  from deepchecks.vision.vision_data.batch_wrapper import BatchWrapper
  from deepchecks.vision.vision_data.format_validators import validate_additional_data_format, validate_embeddings_format, validate_image_identifiers_format, validate_images_format, validate_labels_format, validate_predictions_format
  from deepchecks.vision.vision_data.utils import BatchOutputFormat, LabelMap, get_class_ids_from_numpy_labels, get_class_ids_from_numpy_preds, shuffle_loader
label_desc: Use image node_id 9 for calling the VisionData obj's underlying member method code with example usage: obj.has_additional_data() and returns: self