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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def _default_hparams():
 """A set of basic model hyperparameters."""

  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)