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_df_and_labels(self, dataset, samples):
      samples_hashes = hash_samples(
          normalize_samples(samples, **self._text_normalization_kwargs)
      )
  
      labels = self._get_labels(dataset)
  
      df = pd.DataFrame(
          {
              "hash": samples_hashes,
              "Sample ID": dataset.get_original_text_indexes(),
              "Label": labels,
              "Text": dataset.text,
          }
      )
  
      return df
  
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 6 for calling the ConflictingLabels obj's underlying member method code with example usage: obj._get_df_and_labels(dataset, samples) and returns: df