class_docstr: Find samples that contain tokens unsupported by your tokenizer.
  
  Parameters
  ----------
  tokenizer: t.Any , default: None
      Tokenizer from the HuggingFace transformers library to use for tokenization. If None,
      AutoTokenizer.from_pretrained('bert-base-uncased') will be used.
  group_singleton_words: bool, default: False
      If True, group all words that appear only once in the data into the "Other" category in the display.
  n_most_common : int , default: 5
      Number of most common words with unknown tokens to show in the display.
  n_samples: int, default: 1_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}
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docstr: Get ids of all non-text tokens in the tokenizer.
  
  These include notably the [CLS] token marking the beginning of the sequence, the [SEP] token marking the end
  of the sequence, and the [PAD] token used for padding.
docstr_tok: ["Get","ids","of","all","non-text","tokens","in","the","tokenizer",".","These","include","notably","the","[","CLS","]","token","marking","the","beginning","of","the","sequence",",","the","[","SEP","]","token","marking","the","end","of","the","sequence",",","and","the","[","PAD","]","token","used","for","padding","."]
code: def _get_non_text_token_ids(self):
      """Get ids of all non-text tokens in the tokenizer.
  
      These include notably the [CLS] token marking the beginning of the sequence, the [SEP] token marking the end
      of the sequence, and the [PAD] token used for padding.
      """
      non_text_token_ids = []
      for (
          token_name,
          token,
      ) in self.tokenizer.special_tokens_map.items():
          if token_name not in ["unk_token"]:
              non_text_token_ids.append(
                  self.tokenizer.convert_tokens_to_ids(token)
              )
      return non_text_token_ids
  
imports: import contextlib
  import copy
  import sys
  import typing
  import warnings
  from collections import Counter
  import nltk
  import plotly.graph_objects
  from deepchecks.core import CheckResult, ConditionCategory, ConditionResult
  from deepchecks.core.errors import DeepchecksProcessError, DeepchecksValueError
  from deepchecks.nlp import Context, SingleDatasetCheck
  from deepchecks.nlp._shared_docs import docstrings
  from deepchecks.nlp.text_data import TextData
  from deepchecks.utils.numbers import round_sig
  from deepchecks.utils.strings import format_list, format_percent, truncate_string
label_desc: Use image node_id 4 for calling the UnknownTokens obj's underlying member method code with example usage: obj._get_non_text_token_ids() and returns: non_text_token_ids