code: def load_data(
      data_format: str = "TextData",
      include_properties: bool = True,
      include_embeddings: bool = False,
  ) -> t.Tuple[
      t.Union[TextData, pd.DataFrame], t.Union[TextData, pd.DataFrame]
  ]:
      """Load and returns the SCIERC Abstract NER dataset (token classification).
  
      Parameters
      ----------
      data_format : str, default: 'TextData'
          Represent the format of the returned value. Can be 'TextData'|'Dict'
          'TextData' will return the data as a TextData object
          'Dict' will return the data as a dict of tokenized texts and IOB NER labels
      include_properties : bool, default: True
          If True, the returned data will include properties of the comments. Incompatible with data_format='DataFrame'
      include_embeddings : bool, default: False
          If True, the returned data will include embeddings of the comments. Incompatible with data_format='DataFrame'
  
      Returns
      -------
      train, test : Tuple[Union[TextData, Dict]
          Tuple of two objects represents the dataset split to train and test sets.
      """
      if data_format.lower() not in ["textdata", "dict"]:
          raise ValueError(
              'data_format must be either "TextData" or "Dict"'
          )
      elif data_format.lower() == "dict":
          if include_properties or include_embeddings:
              warnings.warn(
                  'include_properties and include_embeddings are incompatible with data_format="Dict". '
                  "loading only original text data",
                  UserWarning,
              )
              include_properties, include_embeddings = False, False
  
      data = load_all_data()
      train, test = data["train"], data["test"]
      # Delete the pred field of the dictionary
      del train["pred"]
      del test["pred"]
  
      if data_format.lower() != "textdata":
          return train, test
  
      if include_properties:
          train_properties, test_properties = load_properties()
      else:
          train_properties, test_properties = None, None
      if include_embeddings:
          train_embeddings, test_embeddings = load_embeddings()
      else:
          train_embeddings, test_embeddings = None, None
  
      train_ds = TextData(
          tokenized_text=train["text"],
          label=train["text"],
          task_type="token_classification",
          properties=train_properties,
          embeddings=train_embeddings,
      )
      test_ds = TextData(
          tokenized_text=test["text"],
          label=test["text"],
          task_type="token_classification",
          properties=test_properties,
          embeddings=test_embeddings,
      )
  
      return train_ds, test_ds
  
file_path: deepchecks/deepchecks/nlp/datasets/token_classification/scierc_ner.py
imports: import pathlib
  import typing
  import warnings
  import numpy
  import pandas
  from deepchecks.nlp import TextData
  from deepchecks.utils.builtin_datasets_utils import read_and_save_data
label_desc: Use image node_id 5 for calling a global function with example usage: load_data(data_format, include_properties, include_embeddings) and returns: train_ds,  test_ds, train,  test