class_docstr: MultiProblem base class.
class_docstr_tok: ["MultiProblem","base","class","."]
docstr: Generate task_ids for each problem.
  
  These ids correspond to the index of the task in the task_list.
  
  Args:
    encoder_vocab_size: the size of the vocab which is used to compute
      the index offset.
docstr_tok: ["Generate","task_ids","for","each","problem",".","These","ids","correspond","to","the","index","of","the","task","in","the","task_list",".","Args",":","encoder_vocab_size",":","the","size","of","the","vocab","which","is","used","to","compute","the","index","offset","."]
code: def update_task_ids(self, encoder_vocab_size):
      """Generate task_ids for each problem.
  
      These ids correspond to the index of the task in the task_list.
  
      Args:
        encoder_vocab_size: the size of the vocab which is used to compute
          the index offset.
      """
      for idx, task in enumerate(self.task_list):
          task.set_task_id(idx + encoder_vocab_size)
          tf.logging.info(
              "Task %d (%s) has id %d." % (idx, task.name, task.task_id)
          )
  
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 8 for calling the MultiProblem obj's underlying member method code with example usage: obj.update_task_ids(encoder_vocab_size) without return types