class_docstr: MultiProblem base class.
class_docstr_tok: ["MultiProblem","base","class","."]
code: def get_hparams(self, model_hparams=None):
      if self._hparams is not None:
          return self._hparams
      self._hparams = self.task_list[0].get_hparams(model_hparams)
      # Increase the vocab size to account for task ids and modify the modality.
      vocab_size_inc = len(self.task_list)
      vocab_size_inc += self.get_max_num_classes()
      vocab_size = self._hparams.vocabulary["targets"].vocab_size
      new_vocab_size = vocab_size + vocab_size_inc
      if model_hparams.multiproblem_vocab_size > new_vocab_size:
          new_vocab_size = model_hparams.multiproblem_vocab_size
      tf.logging.info("Old vocabulary size: %d" % vocab_size)
      self.update_task_ids(vocab_size)
      tf.logging.info("New vocabulary size: %d" % new_vocab_size)
      self._hparams.vocab_size["targets"] = new_vocab_size
      self._hparams.modality["targets"] = modalities.ModalityType.SYMBOL
      return self._hparams
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from tensor2tensor.data_generators import problem
  from tensor2tensor.data_generators import text_problems
  from tensor2tensor.layers import common_layers
  from tensor2tensor.layers import discretization
  from tensor2tensor.layers import modalities
  from tensor2tensor.utils import metrics
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 5 for calling the MultiProblem obj's underlying member method code with example usage: obj.get_hparams(model_hparams) and returns: self, self