code: def test_forward_works_with_projection_layer(self):
      params = Params(
          {
              "options_file": self.FIXTURES_ROOT
              / "elmo"
              / "options.json",
              "weight_file": self.FIXTURES_ROOT
              / "elmo"
              / "lm_weights.hdf5",
              "projection_dim": 20,
          }
      )
      word1 = [0] * 50
      word2 = [0] * 50
      word1[0] = 6
      word1[1] = 5
      word1[2] = 4
      word1[3] = 3
      word2[0] = 3
      word2[1] = 2
      word2[2] = 1
      word2[3] = 0
      embedding_layer = ElmoTokenEmbedder.from_params(
          vocab=None, params=params
      )
      input_tensor = torch.LongTensor([[word1, word2]])
      embedded = embedding_layer(input_tensor).data.numpy()
      assert embedded.shape == (1, 2, 20)
  
      input_tensor = torch.LongTensor([[[word1]]])
      embedded = embedding_layer(input_tensor).data.numpy()
      assert embedded.shape == (1, 1, 1, 20)
  
imports: from __future__ import with_statement
  from __future__ import division
  from __future__ import absolute_import
  import filecmp
  import json
  import os
  import pathlib
  import tarfile
  import torch
  from allennlp.commands.train import train_model
  from allennlp.common import Params
  from allennlp.common.testing import ModelTestCase
  from allennlp.data.dataset import Batch
  from allennlp.modules.token_embedders import ElmoTokenEmbedder
  from io import open
label_desc: Use image node_id 5 for calling the TestElmoTokenEmbedder obj's underlying member method code with example usage: obj.test_forward_works_with_projection_layer() without return types