class_docstr: Builds AdaNet candidates.
class_docstr_tok: ["Builds","AdaNet","candidates","."]
docstr: Creates a `_CandidateBuilder` instance.
  
  Args:
    adanet_loss_decay: Float. The adanet loss is tracked as an exponential
      moving average, so this is the decay rate to use.
  
  Returns:
    A `_CandidateBuilder` object.
docstr_tok: ["Creates","a","`","_CandidateBuilder","`","instance",".","Args",":","adanet_loss_decay",":","Float",".","The","adanet","loss","is","tracked","as","an","exponential","moving","average",",","so","this","is","the","decay","rate","to","use",".","Returns",":","A","`","_CandidateBuilder","`","object","."]
code: def __init__(self, adanet_loss_decay=0.999):
      """Creates a `_CandidateBuilder` instance.
  
      Args:
        adanet_loss_decay: Float. The adanet loss is tracked as an exponential
          moving average, so this is the decay rate to use.
  
      Returns:
        A `_CandidateBuilder` object.
      """
  
      self._adanet_loss_decay = adanet_loss_decay
      super(_CandidateBuilder, self).__init__()
  
file_path: adanet/adanet/core/candidate.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  from adanet import tf_compat
  import tensorflow.compat.v2
label_desc: Use image node_id 1 to create a new _CandidateBuilder object from inherited base classes: object with example: obj = _CandidateBuilder(adanet_loss_decay)