class_docstr: Generates the indices for the byte-pair encoding used by
  the OpenAI transformer language model: https://blog.openai.com/language-unsupervised/
  
  This is unlike most of our TokenIndexers in that its
  indexing is not based on a `Vocabulary` but on a fixed
  set of mappings that are loaded by the constructor.
class_docstr_tok: ["Generates","the","indices","for","the","byte-pair","encoding","used","by","the","OpenAI","transformer","language","model",":","https",":","\/\/blog.openai.com\/language-unsupervised\/","This","is","unlike","most","of","our","TokenIndexers","in","that","its","indexing","is","not","based","on","a","`","Vocabulary","`","but","on","a","fixed","set","of","mappings","that","are","loaded","by","the","constructor","."]
code: def count_vocab_items(self, token, counter):
      # If we only use pretrained models, we don't need to do anything here.
      pass
  
file_path: magnitude/pymagnitude/third_party/allennlp/data/token_indexers/openai_transformer_byte_pair_indexer.py
imports: from __future__ import with_statement
  from __future__ import absolute_import
  import json
  import tarfile
  from allennlp.common.checks import ConfigurationError
  from allennlp.common.file_utils import cached_path
  from allennlp.common.util import pad_sequence_to_length
  from allennlp.data.vocabulary import Vocabulary
  from allennlp.data.tokenizers.token import Token
  from allennlp.data.token_indexers.token_indexer import TokenIndexer
label_desc: Use image node_id 2 for calling the OpenaiTransformerBytePairIndexer obj's underlying member method code with example usage: obj.count_vocab_items(token, counter) without return types