code: def test_generator_with_distillation_and_intermixed(self):
      # Force graph mode
      with tf.compat.v1.Graph().as_default():
          spec = phoenix_spec_pb2.PhoenixSpec(
              problem_type=phoenix_spec_pb2.PhoenixSpec.CNN
          )
          spec.is_input_shared = True
          spec.search_type = (
              phoenix_spec_pb2.PhoenixSpec.NONADAPTIVE_RANDOM_SEARCH
          )
          spec.ensemble_spec.ensemble_search_type = (
              ensembling_spec_pb2.EnsemblingSpec.INTERMIXED_NONADAPTIVE_ENSEMBLE_SEARCH
          )
          spec.ensemble_spec.intermixed_search.width = 2
          spec.ensemble_spec.intermixed_search.try_ensembling_every = 4
          spec.ensemble_spec.intermixed_search.num_trials_to_consider = (
              3
          )
          spec.distillation_spec.distillation_type = (
              distillation_spec_pb2.DistillationSpec.DistillationType.MSE_LOGITS
          )
          generator = (
              search_candidate_generator.SearchCandidateGenerator(
                  phoenix_spec=spec,
                  metadata=ml_metadata_db.MLMetaData(
                      phoenix_spec=spec, study_name="", study_owner=""
                  ),
              )
          )
          fake_config = collections.namedtuple(
              "RunConfig", ["model_dir", "is_chief"]
          )
          run_config = fake_config(
              model_dir=flags.FLAGS.test_tmpdir + "/10000",
              is_chief=True,
          )
          tf.io.gfile.makedirs(run_config.model_dir)
  
          self._create_checkpoint(["search_generator"], 2)
          self._create_checkpoint(["search_generator"], 3)
          self._create_checkpoint(["search_generator"], 5)
          _ = generator.generate(
              input_layer_fn=lambda: None,
              trial_mode=trial_utils.TrialMode.DISTILLATION,
              logits_dimension=10,
              hparams=hp.HParams(
                  initial_architecture=["CONVOLUTION_3X3"]
              ),
              run_config=run_config,
              is_training=True,
              trials=trial_utils.create_test_trials_intermixed(
                  flags.FLAGS.test_tmpdir
              ),
          )
  
imports: import collections
  import os
  from absl import flags
  from model_search import hparam
  from model_search.architecture import architecture_utils
  from model_search.generators import search_candidate_generator
  from model_search.generators import trial_utils
  from model_search.metadata import ml_metadata_db
  from model_search.metadata import trial
  from model_search.proto import distillation_spec_pb2
  from model_search.proto import ensembling_spec_pb2
  from model_search.proto import phoenix_spec_pb2
  from model_search.proto import transfer_learning_spec_pb2
  import numpy
  import tensorflow.compat.v2
label_desc: Use image node_id 7 for calling the SearchCandidateGeneratorTest obj's underlying member method code with example usage: obj.test_generator_with_distillation_and_intermixed() without return types