class_docstr: Constructs a Ernie-M tokenizer. It uses the `sentencepiece` tools to cut the words to sub-words.
  
  Args:
      sentencepiece_model_file (`str`):
          The file path of sentencepiece model.
      vocab_file (`str`, *optional*):
          The file path of the vocabulary.
      do_lower_case (`str`, *optional*, defaults to `True`):
          Whether or not to lowercase the input when tokenizing.
      unk_token (`str`, *optional*, defaults to `"[UNK]"`):
          A special token representing the `unknown (out-of-vocabulary)` token. An unknown token is set to be
          `unk_token` inorder to be converted to an ID.
      sep_token (`str`, *optional*, defaults to `"[SEP]"`):
          A special token separating two different sentences in the same input.
      pad_token (`str`, *optional*, defaults to `"[PAD]"`):
          A special token used to make arrays of tokens the same size for batching purposes.
      cls_token (`str`, *optional*, defaults to `"[CLS]"`):
          A special token used for sequence classification. It is the last token of the sequence when built with
          special tokens.
      mask_token (`str`, *optional*, defaults to `"[MASK]"`):
          A special token representing a masked token. This is the token used in the masked language modeling task
          which the model tries to predict the original unmasked ones.
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docstr: Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
  adding special tokens. An ErnieM sequence has the following format:
  
  - single sequence: `[CLS] X [SEP]`
  - pair of sequences: `[CLS] A [SEP] [SEP] B [SEP]`
  
  Args:
      token_ids_0 (`List[int]`):
          List of IDs to which the special tokens will be added.
      token_ids_1 (`List[int]`, *optional*):
          Optional second list of IDs for sequence pairs.
  Returns:
      `List[int]`: List of input_id with the appropriate special tokens.
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code: def build_inputs_with_special_tokens(
      self, token_ids_0, token_ids_1=None
  ):
      r"""
      Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
      adding special tokens. An ErnieM sequence has the following format:
  
      - single sequence: `[CLS] X [SEP]`
      - pair of sequences: `[CLS] A [SEP] [SEP] B [SEP]`
  
      Args:
          token_ids_0 (`List[int]`):
              List of IDs to which the special tokens will be added.
          token_ids_1 (`List[int]`, *optional*):
              Optional second list of IDs for sequence pairs.
      Returns:
          `List[int]`: List of input_id with the appropriate special tokens.
      """
      if token_ids_1 is None:
          return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
      _cls = [self.cls_token_id]
      _sep = [self.sep_token_id]
      return _cls + token_ids_0 + _sep + _sep + token_ids_1 + _sep
  
imports: import io
  import os
  import unicodedata
  from typing import Any, Dict, List, Optional, Tuple
  import sentencepiece
  from ...tokenization_utils import PreTrainedTokenizer
  from ...utils import logging
label_desc: Use image node_id 13 for calling the ErnieMTokenizer obj's underlying member method code with example usage: obj.build_inputs_with_special_tokens(token_ids_0, token_ids_1) and returns: unknown, unknown