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_conflicting_indices(df: pd.DataFrame):
      by_hash = df.loc[:, ["hash", "Label"]].groupby(
          ["hash"], dropna=False
      )
      count_labels = lambda x: len(set(x.to_list()))
      n_of_labels_per_sample = by_hash["Label"].aggregate(count_labels)
  
      ambiguous_samples_hashes = n_of_labels_per_sample[
          n_of_labels_per_sample > 1
      ]
      return frozenset(ambiguous_samples_hashes.index.to_list())
  
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 5 for calling the ConflictingLabels obj's underlying member method code with example usage: obj._get_conflicting_indices(df) and returns: frozenset