class_docstr: Vocabulary basically contains mapping between numbers and tokens and vice versa.
  
  Parameters
  ----------
  idx2tok: List[str], required
      List of tokens where list indices are corresponding to
      their mapped integer indices.
  
  use_special_tokens: bool, optional, default: False
      If `True`, vocabulary will include `SPECIAL_TOKENS`.
class_docstr_tok: ["Vocabulary","basically","contains","mapping","between","numbers","and","tokens","and","vice","versa",".","Parameters","--","--","--","--","--","idx2tok",":","List","[","str","]",",","required","List","of","tokens","where","list","indices","are","corresponding","to","their","mapped","integer","indices",".","use_special_tokens",":","bool",",","optional",",","default",":","False","If","`","True","`",",","vocabulary","will","include","`","SPECIAL_TOKENS","`","."]
docstr: Build a vocabulary from sequences (list of list of tokens).
  
  Parameters
  ----------
  sequences: List[List[str]], required
      Corpus of multiple lists of string tokens.
  
  max_vocab: int, optional
      Limit for size of the vocabulary. If specified, tokens will
      be ranked based on counts and gathered top-down until reach `max_vocab`.
  
  min_freq: int, optional, default: 1
      Cut-off threshold for tokens based on their counts.
  
  use_special_tokens: bool, optional, default: False
      If `True`, vocabulary will include `SPECIAL_TOKENS`.
docstr_tok: ["Build","a","vocabulary","from","sequences","(","list","of","list","of","tokens",")",".","Parameters","--","--","--","--","--","sequences",":","List","[","List","[","str","]","]",",","required","Corpus","of","multiple","lists","of","string","tokens",".","max_vocab",":","int",",","optional","Limit","for","size","of","the","vocabulary",".","If","specified",",","tokens","will","be","ranked","based","on","counts","and","gathered","top-down","until","reach","`","max_vocab","`",".","min_freq",":","int",",","optional",",","default",":","1","Cut-off","threshold","for","tokens","based","on","their","counts",".","use_special_tokens",":","bool",",","optional",",","default",":","False","If","`","True","`",",","vocabulary","will","include","`","SPECIAL_TOKENS","`","."]
code: def from_sequences(
      cls,
      sequences: List[List[str]],
      max_vocab: int = None,
      min_freq: int = 1,
      use_special_tokens: bool = False,
  ) -> "Vocabulary":
      """Build a vocabulary from sequences (list of list of tokens).
  
      Parameters
      ----------
      sequences: List[List[str]], required
          Corpus of multiple lists of string tokens.
  
      max_vocab: int, optional
          Limit for size of the vocabulary. If specified, tokens will
          be ranked based on counts and gathered top-down until reach `max_vocab`.
  
      min_freq: int, optional, default: 1
          Cut-off threshold for tokens based on their counts.
  
      use_special_tokens: bool, optional, default: False
          If `True`, vocabulary will include `SPECIAL_TOKENS`.
  
      """
      return Vocabulary.from_tokens(
          [tok for seq in sequences for tok in seq],
          max_vocab,
          min_freq,
          use_special_tokens,
      )
  
imports: from typing import List, Dict, Callable, Union
  from collections import defaultdict, Counter, OrderedDict
  import string
  import pickle
  import re
  import numpy
  import scipy.sparse
  from .None import FeatureModality
  from .modality import fallback_feature
  from ..utils import normalize
label_desc: Use image node_id 10 for calling the Vocabulary obj's underlying member method code with example usage: obj.from_sequences(cls, sequences, max_vocab, min_freq, use_special_tokens) and returns: Vocabulary