class_docstr: Interface for a candidate subnetwork generator.
  
  Given the ensemble of subnetworks at iteration t-1, this object is
  responsible for generating the set of candidate subnetworks for iteration t
  that minimize the objective as part of an ensemble.
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docstr: Generates :class:`adanet.subnetwork.Builder` instances for an iteration.
  
  NOTE: Every call to :meth:`generate_candidates` must be deterministic for
  the given arguments.
  
  Args:
    previous_ensemble: The best :class:`adanet.Ensemble` from iteration t-1.
      DEPRECATED. We are transitioning away from the use of previous_ensemble
      in generate_candidates. New Generators should *not* use
      previous_ensemble in their implementation of generate_candidates --
      please only use iteration_number, previous_ensemble_reports and
      all_reports.
    iteration_number: Python integer AdaNet iteration t, starting from 0.
    previous_ensemble_reports: List of
      :class:`adanet.subnetwork.MaterializedReport` instances corresponding to
      the Builders composing :class:`adanet.Ensemble` from iteration t-1. The
      first element in the list corresponds to the Builder added in the
      first iteration. If a :class:`adanet.subnetwork.MaterializedReport` is
      not supplied to the estimator, previous_ensemble_report is `None`.
    all_reports: List of :class:`adanet.subnetwork.MaterializedReport`
      instances. If an :class:`adanet.subnetwork.ReportMaterializer` is not
      supplied to the estimator, `all_reports` is `None`. If
      :class:`adanet.subnetwork.ReportMaterializer` is supplied to the
      estimator and t=0, `all_reports` is an empty List. Otherwise,
      `all_reports` is a sequence of Lists. Each element of the sequence is a
      List containing all the :class:`adanet.subnetwork.MaterializedReport`
      instances in an AdaNet iteration, starting from iteration 0, and
      ending at iteration t-1.
    config: The current :class:`tf.estimator.RunConfig` object to configure
      the runtime settings.
  
  Returns:
    A list of :class:`adanet.subnetwork.Builder` instances.
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code: def generate_candidates(
      self,
      previous_ensemble,
      iteration_number,
      previous_ensemble_reports,
      all_reports,
      config,
  ):
      # pyformat: disable
      """Generates :class:`adanet.subnetwork.Builder` instances for an iteration.
  
      NOTE: Every call to :meth:`generate_candidates` must be deterministic for
      the given arguments.
  
      Args:
        previous_ensemble: The best :class:`adanet.Ensemble` from iteration t-1.
          DEPRECATED. We are transitioning away from the use of previous_ensemble
          in generate_candidates. New Generators should *not* use
          previous_ensemble in their implementation of generate_candidates --
          please only use iteration_number, previous_ensemble_reports and
          all_reports.
        iteration_number: Python integer AdaNet iteration t, starting from 0.
        previous_ensemble_reports: List of
          :class:`adanet.subnetwork.MaterializedReport` instances corresponding to
          the Builders composing :class:`adanet.Ensemble` from iteration t-1. The
          first element in the list corresponds to the Builder added in the
          first iteration. If a :class:`adanet.subnetwork.MaterializedReport` is
          not supplied to the estimator, previous_ensemble_report is `None`.
        all_reports: List of :class:`adanet.subnetwork.MaterializedReport`
          instances. If an :class:`adanet.subnetwork.ReportMaterializer` is not
          supplied to the estimator, `all_reports` is `None`. If
          :class:`adanet.subnetwork.ReportMaterializer` is supplied to the
          estimator and t=0, `all_reports` is an empty List. Otherwise,
          `all_reports` is a sequence of Lists. Each element of the sequence is a
          List containing all the :class:`adanet.subnetwork.MaterializedReport`
          instances in an AdaNet iteration, starting from iteration 0, and
          ending at iteration t-1.
        config: The current :class:`tf.estimator.RunConfig` object to configure
          the runtime settings.
  
      Returns:
        A list of :class:`adanet.subnetwork.Builder` instances.
      """
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import abc
  import collections
  import six
label_desc: Use image node_id 1 for calling the Generator obj's underlying member method code with example usage: obj.generate_candidates(previous_ensemble, iteration_number, previous_ensemble_reports, all_reports, config) without return types