code: def _default_hparams():
    
    return hparam.HParams(
        # Use this parameter to get comparable perplexity numbers with different
        # tokenizations.  This value should be set to the ratio of the number of
        # tokens in the test set according to the tokenization used to the number
        # of tokens in the test set in the "official" tokenization.  For
        # example, if we are using a word-piece based model and we want to
        # compute per-word perplexity, then we set loss_multiplier to the number
        # of wordpieces per word in the test set.
        loss_multiplier=1.0,
  
        # Use this parameter to allow for larger sequences in the batch. Without
        # the use of this parameter, the size of the inner two dimensions will
        # be used to judge the sequence length.
        batch_size_multiplier=1,
  
        # During inference for autoregressive problems, if the batch_size is 1,
        # the inference will stop when the model predict a text_encoder.EOS_ID
        # token.
        stop_at_eos=False,
  
        # Modalities used to map from features to a space compatible with
        # chosen model architecture. It comprises key-value pairs of a feature
        # name (str) and its modality type.
        modality={},
        vocab_size={},
  
        # Identifiers used to tell the model which input/target space will be
        # expected. For example, it can tell that we expect French as characters
        # as output, or Spanish as sound. Spaces defined as constants in SpaceID
        # class.
        input_space_id=SpaceID.GENERIC,
        target_space_id=SpaceID.GENERIC)
docs: A set of basic model hyperparameters.