class_docstr: Dictionary encapsulates the mapping between normalized words and their integer ids.
  
  Notable instance attributes:
  
  Attributes
  ----------
  token2id : dict of (str, int)
      token -> token_id. I.e. the reverse mapping to `self[token_id]`.
  cfs : dict of (int, int)
      Collection frequencies: token_id -> how many instances of this token are contained in the documents.
  dfs : dict of (int, int)
      Document frequencies: token_id -> how many documents contain this token.
  num_docs : int
      Number of documents processed.
  num_pos : int
      Total number of corpus positions (number of processed words).
  num_nnz : int
      Total number of non-zeroes in the BOW matrix (sum of the number of unique
      words per document over the entire corpus).
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docstr: Get all stored ids.
  
  Returns
  -------
  list of int
      List of all token ids.
docstr_tok: ["Get","all","stored","ids",".","Returns","--","--","--","-","list","of","int","List","of","all","token","ids","."]
code: def keys(self):
      """Get all stored ids.
  
      Returns
      -------
      list of int
          List of all token ids.
  
      """
      return list(self.token2id.values())
  
imports: from collections import defaultdict
  from collections.abc import Mapping
  import logging
  import itertools
  from typing import Optional, List, Tuple
  from gensim import utils
label_desc: Use image node_id 6 for calling the Dictionary obj's underlying member method code with example usage: obj.keys() and returns: list