class_docstr: Find identical samples which have different labels.
  
  Parameters
  ----------
  {text_normalization_params:1*indent}
  n_to_show : int , default: 5
      number of most common ambiguous samples to show.
  n_samples : int , default: 10_000_000
      number of samples to use for this check.
  random_state : int, default: 42
      random seed for all check internals.
  {max_text_length_for_display_param:1*indent}
code: def _get_labels(self, dataset):
      if dataset.task_type is TaskType.TOKEN_CLASSIFICATION:
          labels = [
              tuple(t.cast(t.Sequence[t.Any], it))
              for it in dataset.label
          ]
      elif dataset.is_multi_label_classification():
          labels = [
              tuple(np.where(row == 1)[0]) for row in dataset.label
          ]
      elif dataset.task_type is TaskType.TEXT_CLASSIFICATION:
          labels = dataset.label
      elif dataset.task_type is TaskType.OTHER:
          raise DeepchecksValueError(
              "Check is irrelevant when task type is not specified"
          )
      else:
          raise DeepchecksValueError(
              f"Unknown task type - {dataset.task_type}"
          )
      return labels
  
imports: import typing
  import numpy
  import pandas
  from deepchecks.core import CheckResult
  from deepchecks.core.errors import DeepchecksValueError
  from deepchecks.nlp import Context, SingleDatasetCheck
  from deepchecks.nlp._shared_docs import docstrings
  from deepchecks.nlp.task_type import TaskType
  from deepchecks.nlp.text_data import TextData
  from deepchecks.nlp.utils.text import cut_string, hash_samples, normalize_samples
  from deepchecks.utils.abstracts.conflicting_labels import ConflictingLabelsAbstract
  from deepchecks.utils.other import to_ordional_enumeration
  from deepchecks.utils.strings import format_list, truncate_string
label_desc: Use image node_id 4 for calling the ConflictingLabels obj's underlying member method code with example usage: obj._get_labels(dataset) and returns: labels