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 __init__(
      self,
      ignore_case: bool = True,
      remove_punctuation: bool = True,
      normalize_unicode: bool = True,
      remove_stopwords: bool = True,
      ignore_whitespace: bool = False,
      n_to_show: int = 5,
      n_samples: int = 10_000_000,
      random_state: int = 42,
      max_text_length_for_display: int = 30,
      **kwargs,
  ):
      super().__init__(**kwargs)
      self.ignore_case = ignore_case
      self.remove_punctuation = remove_punctuation
      self.normalize_unicode = normalize_unicode
      self.remove_stopwords = remove_stopwords
      self.ignore_whitespace = ignore_whitespace
      self.n_to_show = n_to_show
      self.n_samples = n_samples
      self.random_state = random_state
      self.max_text_length_for_display = max_text_length_for_display
  
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 1 to create a new ConflictingLabels object from inherited base classes: SingleDatasetCheck, ConflictingLabelsAbstract with example: obj = ConflictingLabels(ignore_case, remove_punctuation, normalize_unicode, remove_stopwords, ignore_whitespace, n_to_show, n_samples, random_state, max_text_length_for_display)