class_docstr: Compute a single layer of ELMo representations.
  
  This class serves as a convenience when you only want to use one layer of
  ELMo representations at the input of your network.  It's essentially a wrapper
  around Elmo(num_output_representations=1, ...)
  
  Registered as a `TokenEmbedder` with name "elmo_token_embedder".
  
  # Parameters
  
  options_file : `str`, required.
      An ELMo JSON options file.
  weight_file : `str`, required.
      An ELMo hdf5 weight file.
  do_layer_norm : `bool`, optional.
      Should we apply layer normalization (passed to `ScalarMix`)?
  dropout : `float`, optional, (default = `0.5`).
      The dropout value to be applied to the ELMo representations.
  requires_grad : `bool`, optional
      If True, compute gradient of ELMo parameters for fine tuning.
  projection_dim : `int`, optional
      If given, we will project the ELMo embedding down to this dimension.  We recommend that you
      try using ELMo with a lot of dropout and no projection first, but we have found a few cases
      where projection helps (particularly where there is very limited training data).
  vocab_to_cache : `List[str]`, optional.
      A list of words to pre-compute and cache character convolutions
      for. If you use this option, the ElmoTokenEmbedder expects that you pass word
      indices of shape (batch_size, timesteps) to forward, instead
      of character indices. If you use this option and pass a word which
      wasn't pre-cached, this will break.
  scalar_mix_parameters : `List[int]`, optional, (default=`None`)
      If not `None`, use these scalar mix parameters to weight the representations
      produced by different layers. These mixing weights are not updated during
      training. The mixing weights here should be the unnormalized (i.e., pre-softmax)
      weights. So, if you wanted to use only the 1st layer of a 2-layer ELMo,
      you can set this to [-9e10, 1, -9e10 ].
docstr: # Parameters
  
  elmo_tokens : `torch.Tensor`
      Shape `(batch_size, timesteps, 50)` of character ids representing the current batch.
  word_inputs : `torch.Tensor`, optional.
      If you passed a cached vocab, you can in addition pass a tensor of shape
      `(batch_size, timesteps)`, which represent word ids which have been pre-cached.
  
  # Returns
  
  `torch.Tensor`
      The ELMo representations for the input sequence, shape
      `(batch_size, timesteps, embedding_dim)`
code: def forward(
      self, elmo_tokens: torch.Tensor, word_inputs: torch.Tensor = None
  ) -> torch.Tensor:
      """
      # Parameters
  
      elmo_tokens : `torch.Tensor`
          Shape `(batch_size, timesteps, 50)` of character ids representing the current batch.
      word_inputs : `torch.Tensor`, optional.
          If you passed a cached vocab, you can in addition pass a tensor of shape
          `(batch_size, timesteps)`, which represent word ids which have been pre-cached.
  
      # Returns
  
      `torch.Tensor`
          The ELMo representations for the input sequence, shape
          `(batch_size, timesteps, embedding_dim)`
      """
      elmo_output = self._elmo(elmo_tokens, word_inputs)
      elmo_representations = elmo_output["elmo_representations"][0]
      if self._projection:
          projection = self._projection
          for _ in range(elmo_representations.dim() - 2):
              projection = TimeDistributed(projection)
          elmo_representations = projection(elmo_representations)
      return elmo_representations
  
file_path: allennlp/allennlp/modules/token_embedders/elmo_token_embedder.py
imports: from typing import List
  import torch
  from allennlp.modules.token_embedders.token_embedder import TokenEmbedder
  from allennlp.modules.elmo import Elmo
  from allennlp.modules.time_distributed import TimeDistributed
label_desc: Use image node_id 3 for calling the ElmoTokenEmbedder obj's underlying member method code with example usage: obj.forward(elmo_tokens, word_inputs) and returns: elmo_representations