class_docstr: Helper class to understand Replay state.
class_docstr_tok: ["Helper","class","to","understand","Replay","state","."]
docstr: Returns true if we are importing a tower in this replay trial.
  
  Examples:
    1. For adaptive ensembling, we import towers for every trial with id
    greater than 1.
    2. For non-adaptive ensembling, we import towers only in the last trial.
  
  Args:
    my_id: trial id.
  
  Returns:
    True if we are importing a tower and False otherwise.
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code: def replay_is_importing_towers(self, my_id):
      """Returns true if we are importing a tower in this replay trial.
  
      Examples:
        1. For adaptive ensembling, we import towers for every trial with id
        greater than 1.
        2. For non-adaptive ensembling, we import towers only in the last trial.
  
      Args:
        my_id: trial id.
  
      Returns:
        True if we are importing a tower and False otherwise.
      """
      towers_number = len(self._phoenix_spec.replay.towers)
      output = (my_id == towers_number and towers_number > 1) or (
          my_id > 1
          and trial_utils.adaptive_or_residual_ensemble(
              self._phoenix_spec
          )
      )
      return output
  
imports: import abc
  import collections
  from absl import logging
  from model_search.architecture import architecture_utils
  from model_search.generators import base_tower_generator
  from model_search.generators import trial_utils
  from model_search.generators.prior_generator import PriorGenerator
  from model_search.generators.replay_generator import ReplayGenerator
  from model_search.generators.search_candidate_generator import SearchCandidateGenerator
label_desc: Use image node_id 5 for calling the ReplayState obj's underlying member method code with example usage: obj.replay_is_importing_towers(my_id) and returns: output