class_docstr: Tokenizes the text with respect to existent phrases in the vocab.
  
  This tokenizers respects phrases that are in the vocab. Phrases are separated with 'ngram_separator', for example,
  in Google News word2vec file, ngrams are separated with a _ like New_York. These phrases are detected in text and merged as one special token. (New York is the ... => [New_York, is, the])
code: def load(input_path: str):
      with open(
          os.path.join(input_path, "phrasetokenizer_config.json"), "r"
      ) as fIn:
          config = json.load(fIn)
  
      return PhraseTokenizer(**config)
  
file_path: sentence-transformers/sentence_transformers/models/tokenizer/PhraseTokenizer.py
imports: from typing import List, Iterable
  import collections
  import string
  import os
  import json
  import logging
  from .WordTokenizer import WordTokenizer, ENGLISH_STOP_WORDS
  import nltk
label_desc: Use image node_id 6 for calling the PhraseTokenizer obj's underlying member method code with example usage: obj.load(input_path) and returns: PhraseTokenizer