class_docstr: MultiProblem base class.
class_docstr_tok: ["MultiProblem","base","class","."]
docstr: Normalize the examples from different tasks so they can be merged.
docstr_tok: ["Normalize","the","examples","from","different","tasks","so","they","can","be","merged","."]
code: def normalize_example(
      self, task, example, encoder, hparams, is_infer
  ):
      """Normalize the examples from different tasks so they can be merged."""
      # Here we use the default function for NLP tasks that makes everything
      # a part of "targets" feature. Override in your subclasses for other uses.
      vocab_offset = encoder.vocab_size + len(self.task_list)
      return normalize_example_nlp(
          task,
          example,
          is_infer,
          self.vocab_type,
          vocab_offset,
          hparams.multiproblem_max_input_length,
          hparams.multiproblem_max_target_length,
          hparams.multiproblem_fixed_train_length,
      )
  
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 3 for calling the MultiProblem obj's underlying member method code with example usage: obj.normalize_example(task, example, encoder, hparams, is_infer) and returns: normalize_example_nlp