code: def _read_embeddings_from_hdf5(embeddings_filename: str,
                                 embedding_dim: int,
                                 vocab: Vocabulary,
                                 namespace: str = "tokens") -> torch.FloatTensor:
      
      with h5py.File(embeddings_filename, 'r') as fin:
          embeddings = fin['embedding'][...]
  
      if list(embeddings.shape) != [vocab.get_vocab_size(namespace), embedding_dim]:
          raise ConfigurationError(
                  "Read shape {0} embeddings from the file, but expected {1}".format(
                          list(embeddings.shape), [vocab.get_vocab_size(namespace), embedding_dim]))
  
      return torch.FloatTensor(embeddings)
docs: Reads from a hdf5 formatted file. The embedding matrix is assumed to
      be keyed by 'embedding' and of size ``(num_tokens, embedding_dim)``.