docstr: Load the test sentences and the expected LM embeddings.
  
  These files loaded in this method were created with a batch-size of 3.
  Due to idiosyncrasies with TensorFlow, the 30 sentences in sentences.json are split into 3 files in which
  the k-th sentence in each is from batch k.
  
  This method returns a (sentences, embeddings) pair where each is a list of length batch_size.
  Each list contains a sublist with total_sentence_count / batch_size elements.  As with the original files,
  the k-th element in the sublist is in batch k.
code: def _load_sentences_embeddings(self):
      """
      Load the test sentences and the expected LM embeddings.
  
      These files loaded in this method were created with a batch-size of 3.
      Due to idiosyncrasies with TensorFlow, the 30 sentences in sentences.json are split into 3 files in which
      the k-th sentence in each is from batch k.
  
      This method returns a (sentences, embeddings) pair where each is a list of length batch_size.
      Each list contains a sublist with total_sentence_count / batch_size elements.  As with the original files,
      the k-th element in the sublist is in batch k.
      """
      with open(self.sentences_json_file) as fin:
          sentences = json.load(fin)
  
      # the expected embeddings
      expected_lm_embeddings = []
      for k in range(len(sentences)):
          embed_fname = os.path.join(
              self.elmo_fixtures_path, "lm_embeddings_{}.hdf5".format(k)
          )
          expected_lm_embeddings.append([])
          with h5py.File(embed_fname, "r") as fin:
              for i in range(10):
                  sent_embeds = fin["%s" % i][...]
                  sent_embeds_concat = numpy.concatenate(
                      (sent_embeds[0, :, :], sent_embeds[1, :, :]),
                      axis=-1,
                  )
                  expected_lm_embeddings[-1].append(sent_embeds_concat)
  
      return sentences, expected_lm_embeddings
  
imports: import json
  import os
  import warnings
  from typing import List
  import numpy
  import torch
  from allennlp.common.testing import AllenNlpTestCase
  from allennlp.data import Instance, Token, Vocabulary
  from allennlp.data.batch import Batch
  from allennlp.data.fields import TextField
  from allennlp.data.token_indexers.elmo_indexer import ELMoTokenCharactersIndexer
  from allennlp.data.token_indexers.single_id_token_indexer import SingleIdTokenIndexer
  from allennlp.data.data_loaders import SimpleDataLoader
  from allennlp.modules.elmo import _ElmoBiLm, _ElmoCharacterEncoder, Elmo
  from allennlp.modules.token_embedders import ElmoTokenEmbedder
  from allennlp.nn.util import remove_sentence_boundaries
label_desc: Use image node_id 2 for calling the ElmoTestCase obj's underlying member method code with example usage: obj._load_sentences_embeddings() and returns: sentences,  expected_lm_embeddings