code: def multilabel_dataset_with_conflicts() -> ProblematicDataset:
      # TODO: refactor, reduce code redundancy
      return ProblematicDataset(
          dataset=TextData(
              label=[
                  [0, 0, 0],
                  [0, 0, 0],
                  [0, 1, 0],
                  [0, 1, 0],
                  [0, 1, 1],
              ],
              task_type="text_classification",
              raw_text=[
                  "Readability counts.",
                  "Readability counts to.",
                  "Errors should never pass silently.",
                  "There should be one-- and preferably only one --obvious way to do it.",
                  "ERRORS, should never pass silently!!",
              ],
          ),
          ambiguous_samples_ratio=0.4,
          ambiguous_samples=[
              # NOTE:
              # tests depend on items order in this list
              AmbiguousDuplicatVariant(
                  labels=[(1,), (1, 2)],
                  sample_ids=[2, 4],
                  text=[
                      "Errors should never pass silently.",
                      "ERRORS, should never pass silently!!",
                  ],
              )
          ],
      )
  
imports: import typing
  import pandas
  import pytest
  from hamcrest import *
  from deepchecks.nlp.checks import ConflictingLabels
  from deepchecks.nlp.text_data import TextData
  from deepchecks.utils.strings import format_percent
  from tests.base.utils import equal_condition_result
label_desc: Use image node_id 4 for calling a global function with example usage: multilabel_dataset_with_conflicts() and returns: ProblematicDataset