class_docstr: Defines how to deterministically replay an AdaNet model search.
  
  Specifically, it reconstructs the previous model and trains its components
  in the correct order without performing any search.
  
  Args:
    best_ensemble_indices: A list of the best ensemble indices (one per
      iteration).
  
  Returns:
    An :class:`adanet.replay.Config` instance.
class_docstr_tok: ["Defines","how","to","deterministically","replay","an","AdaNet","model","search",".","Specifically",",","it","reconstructs","the","previous","model","and","trains","its","components","in","the","correct","order","without","performing","any","search",".","Args",":","best_ensemble_indices",":","A","list","of","the","best","ensemble","indices","(","one","per","iteration",")",".","Returns",":","An",":","class",":","`","adanet.replay.Config","`","instance","."]
code: def __init__(self, best_ensemble_indices=None):
      self._best_ensemble_indices = best_ensemble_indices
  
file_path: adanet/adanet/replay/__init__.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import json
  import os
  import tensorflow.compat.v1
label_desc: Use image node_id 1 to create a new Config object from inherited base classes: object with example: obj = Config(best_ensemble_indices)