class_docstr: Compute context insensitive token representation using pretrained biLM.
  
  This embedder has input character ids of size (batch_size, sequence_length, 50)
  and returns (batch_size, sequence_length + 2, embedding_dim), where embedding_dim
  is specified in the options file (typically 512).
  
  We add special entries at the beginning and end of each sequence corresponding
  to <S> and </S>, the beginning and end of sentence tokens.
  
  Note: this is a lower level class useful for advanced usage.  Most users should
  use ``ElmoTokenEmbedder`` or ``allennlp.modules.Elmo`` instead.
  
  Parameters
  ----------
  options_file : ``str``
      ELMo JSON options file
  weight_file : ``str``
      ELMo hdf5 weight file
  requires_grad: ``bool``, optional
      If True, compute gradient of ELMo parameters for fine tuning.
  
  The relevant section of the options file is something like:
  .. example-code::
  
      .. code-block:: python
  
          {'char_cnn': {
              'activation': 'relu',
              'embedding': {'dim': 4},
              'filters': [[1, 4], [2, 8], [3, 16], [4, 32], [5, 64]],
              'max_characters_per_token': 50,
              'n_characters': 262,
              'n_highway': 2
              }
          }
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code: def _load_char_embedding(self):
      with h5py.File(cached_path(self._weight_file), "r") as fin:
          char_embed_weights = fin["char_embed"][...]
  
      weights = numpy.zeros(
          (
              char_embed_weights.shape[0] + 1,
              char_embed_weights.shape[1],
          ),
          dtype="float32",
      )
      weights[1:, :] = char_embed_weights
  
      self._char_embedding_weights = torch.nn.Parameter(
          torch.FloatTensor(weights), requires_grad=self.requires_grad
      )
  
imports: from __future__ import with_statement
  from __future__ import absolute_import
  import json
  import logging
  import warnings
  import torch
  from torch.nn.modules import Dropout
  import numpy
  from io import open
  from allennlp.common.file_utils import cached_path
  from allennlp.common.checks import ConfigurationError
  from allennlp.common import Params
  from allennlp.common.util import lazy_groups_of
  from allennlp.modules.elmo_lstm import ElmoLstm
  from allennlp.modules.highway import Highway
  from allennlp.modules.scalar_mix import ScalarMix
  from allennlp.nn.util import remove_sentence_boundaries, add_sentence_boundary_token_ids, get_device_of
  from allennlp.data.token_indexers.elmo_indexer import ELMoCharacterMapper, ELMoTokenCharactersIndexer
  from allennlp.data.dataset import Batch
  from allennlp.data import Token, Vocabulary, Instance
  from allennlp.data.fields import TextField
label_desc: Use image node_id 5 for calling the _ElmoCharacterEncoder obj's underlying member method code with example usage: obj._load_char_embedding() without return types