code: def load_under_annotated_data():
      """Load and return the test data, modified to have under annotated segment."""
      _, test = load_data()
      test_copy = test.copy()
  
      # randomly remove 5% of the labels
      np.random.seed(42)
      idx_to_fillna = np.random.choice(
          range(len(test)), int(len(test) * 0.05), replace=False
      )
      test_copy._label = test_copy._label.astype(
          dtype=object
      )  # pylint: disable=protected-access
      test_copy._label[
          idx_to_fillna
      ] = None  # pylint: disable=protected-access
  
      # randomly remove 40% of the under annotated segments
      np.random.seed(42)
      under_annotated_segment_idx = test_copy.properties[
          (test_copy.properties.Fluency < 0.4)
          & (test_copy.properties.Formality < 0.2)
      ].index
      idx_to_fillna = np.random.choice(
          under_annotated_segment_idx,
          int(len(under_annotated_segment_idx) * 0.4),
          replace=False,
      )
      test_copy._label[
          idx_to_fillna
      ] = None  # pylint: disable=protected-access
      return test_copy
  
file_path: deepchecks/deepchecks/nlp/datasets/classification/tweet_emotion.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_under_annotated_data() and returns: test_copy