class_docstr: Construct a FastSpeech2Conformer tokenizer.
  
  Args:
      vocab_file (`str`):
          Path to the vocabulary file.
      bos_token (`str`, *optional*, defaults to `"<sos/eos>"`):
          The begin of sequence token. Note that for FastSpeech2, it is the same as the `eos_token`.
      eos_token (`str`, *optional*, defaults to `"<sos/eos>"`):
          The end of sequence token. Note that for FastSpeech2, it is the same as the `bos_token`.
      pad_token (`str`, *optional*, defaults to `"<blank>"`):
          The token used for padding, for example when batching sequences of different lengths.
      unk_token (`str`, *optional*, defaults to `"<unk>"`):
          The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
          token instead.
      should_strip_spaces (`bool`, *optional*, defaults to `False`):
          Whether or not to strip the spaces from the list of tokens.
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docstr: Converts a token (str) in an id using the vocab.
docstr_tok: ["Converts","a","token","(","str",")","in","an","id","using","the","vocab","."]
code: def _convert_token_to_id(self, token):
      """Converts a token (str) in an id using the vocab."""
      return self.encoder.get(token, self.encoder.get(self.unk_token))
  
file_path: transformers/src/transformers/models/fastspeech2_conformer/tokenization_fastspeech2_conformer.py
imports: import json
  import os
  from typing import Optional, Tuple
  import regex
  from ...tokenization_utils import PreTrainedTokenizer
  from ...utils import logging, requires_backends
label_desc: Use image node_id 6 for calling the FastSpeech2ConformerTokenizer obj's underlying member method code with example usage: obj._convert_token_to_id(token) and returns: self