class_docstr: Indexing for text tokens.
  
  
  Build indices for the unknown token, reserved tokens, and input counter keys. Indexed tokens can
  be used by token embeddings.
  
  
  Parameters
  ----------
  counter : collections.Counter or None, default None
      Counts text token frequencies in the text data. Its keys will be indexed according to
      frequency thresholds such as `most_freq_count` and `min_freq`. Keys of `counter`,
      `unknown_token`, and values of `reserved_tokens` must be of the same hashable type.
      Examples: str, int, and tuple.
  most_freq_count : None or int, default None
      The maximum possible number of the most frequent tokens in the keys of `counter` that can be
      indexed. Note that this argument does not count any token from `reserved_tokens`. Suppose
      that there are different keys of `counter` whose frequency are the same, if indexing all of
      them will exceed this argument value, such keys will be indexed one by one according to
      their __cmp__() order until the frequency threshold is met. If this argument is None or
      larger than its largest possible value restricted by `counter` and `reserved_tokens`, this
      argument has no effect.
  min_freq : int, default 1
      The minimum frequency required for a token in the keys of `counter` to be indexed.
  unknown_token : hashable object, default '&lt;unk&gt;'
      The representation for any unknown token. In other words, any unknown token will be indexed
      as the same representation. Keys of `counter`, `unknown_token`, and values of
      `reserved_tokens` must be of the same hashable type. Examples: str, int, and tuple.
  reserved_tokens : list of hashable objects or None, default None
      A list of reserved tokens that will always be indexed, such as special symbols representing
      padding, beginning of sentence, and end of sentence. It cannot contain `unknown_token`, or
      duplicate reserved tokens. Keys of `counter`, `unknown_token`, and values of
      `reserved_tokens` must be of the same hashable type. Examples: str, int, and tuple.
  
  
  Attributes
  ----------
  unknown_token : hashable object
      The representation for any unknown token. In other words, any unknown token will be indexed
      as the same representation.
  reserved_tokens : list of strs or None
      A list of reserved tokens that will always be indexed.
docstr: list of strs:  A list of indexed tokens where the list indices and the token indices are aligned.
docstr_tok: ["list","of","strs",":","A","list","of","indexed","tokens","where","the","list","indices","and","the","token","indices","are","aligned","."]
code: def idx_to_token(self):
      """
      list of strs:  A list of indexed tokens where the list indices and the token indices are aligned.
      """
      return self._idx_to_token
  
file_path: mxnet/python/mxnet/contrib/text/vocab.py
imports: import collections
  from .None import _constants
label_desc: Use image node_id 6 for calling the Vocabulary obj's underlying member method code with example usage: obj.idx_to_token() and returns: self