class_docstr: Generates a list of `Builders`.
docstr: See `adanet.subnetwork.Generator`.
docstr_tok: ["See","`","adanet.subnetwork.Generator","`","."]
code: def generate_candidates(
      self,
      previous_ensemble,
      iteration_number,
      previous_ensemble_reports,
      all_reports,
  ):
      """See `adanet.subnetwork.Generator`."""
  
      num_cells = self._hparams.num_cells
      num_conv_filters = self._hparams.num_conv_filters
      # Get the architecture of the last subnetwork.
      if previous_ensemble:
          num_cells = int(
              subnetwork_utils.get_persisted_value_from_ensemble(
                  previous_ensemble, _PREVIOUS_NUM_CELLS
              )
          )
  
          num_conv_filters = int(
              subnetwork_utils.get_persisted_value_from_ensemble(
                  previous_ensemble, _PREVIOUS_CONV_FILTERS
              )
          )
  
      candidates = [
          self._builder_fn(
              hparams=subnetwork_utils.copy_update(
                  self._hparams,
                  num_cells=num_cells + 3,
                  num_conv_filters=num_conv_filters,
              )
          ),
          self._builder_fn(
              hparams=subnetwork_utils.copy_update(
                  self._hparams,
                  num_cells=num_cells,
                  num_conv_filters=num_conv_filters + 10,
              )
          ),
      ]
      return candidates
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import functools
  import adanet
  import tensorflow.compat.v1
label_desc: Use image node_id 2 for calling the DynamicGenerator obj's underlying member method code with example usage: obj.generate_candidates(previous_ensemble, iteration_number, previous_ensemble_reports, all_reports) and returns: candidates