class_docstr: A ``TokenIndexer`` determines how string tokens get represented as arrays of indices in a model.
  This class both converts strings into numerical values, with the help of a
  :class:`~allennlp.data.vocabulary.Vocabulary`, and it produces actual arrays.
  
  Tokens can be represented as single IDs (e.g., the word "cat" gets represented by the number
  34), or as lists of character IDs (e.g., "cat" gets represented by the numbers [23, 10, 18]),
  or in some other way that you can come up with (e.g., if you have some structured input you
  want to represent in a special way in your data arrays, you can do that here).
docstr: This method returns a padding dictionary for the given token that specifies lengths for
  all arrays that need padding.  For example, for single ID tokens the returned dictionary
  will be empty, but for a token characters representation, this will return the number
  of characters in the token.
code: def get_padding_lengths(self, token):
      """
      This method returns a padding dictionary for the given token that specifies lengths for
      all arrays that need padding.  For example, for single ID tokens the returned dictionary
      will be empty, but for a token characters representation, this will return the number
      of characters in the token.
      """
      raise NotImplementedError
  
file_path: magnitude/pymagnitude/third_party/allennlp/data/token_indexers/token_indexer.py
imports: from __future__ import absolute_import
  from allennlp.common import Registrable
  from allennlp.data.tokenizers.token import Token
  from allennlp.data.vocabulary import Vocabulary
label_desc: Use image node_id 4 for calling the TokenIndexer obj's underlying member method code with example usage: obj.get_padding_lengths(token) without return types