code: def test_learning_spec_on_predict(
      self, learning_rate_spec, not_containing
  ):
      spec = phoenix_spec_pb2.PhoenixSpec(
          problem_type=phoenix_spec_pb2.PhoenixSpec.DNN
      )
      task_manager_instance = task_manager.TaskManager(
          spec,
          logits_dimension=None,
          loss_fn=loss_fns.make_multi_class_loss_fn(),
          head=None,
      )
      logits = tf.keras.layers.Dense(10)(tf.zeros([20, 10]))
      logits_spec = architecture_utils.LogitsSpec(logits=logits)
      fake_tower = collections.namedtuple("fake_tower", ["logits_spec"])
      towers = {"search_generator": [fake_tower(logits_spec)]}
      features = {"x": tf.zeros([10, 10])}
      model = task_manager_instance.create_model_spec(
          features=features,
          params=hp.HParams(optimizer="sgd"),
          learning_rate_spec=learning_rate_spec,
          towers=towers,
          labels=tf.ones([20], dtype=tf.int32),
          mode=tf.estimator.ModeKeys.PREDICT,
          model_directory=self.get_temp_dir(),
          use_tpu=False,
          my_id=1,
          trial_mode=trial_utils.TrialMode.NO_PRIOR,
          predictions_fn=_default_predictions_fn,
      )
      for phrase in not_containing:
          self.assertEmpty(
              [
                  node.name
                  for node in tf.compat.v1.get_default_graph()
                  .as_graph_def()
                  .node
                  if phrase in node.name
              ]
          )
      self.assertLen(model.predictions, 3)
      self.assertIn("probabilities", model.predictions)
      self.assertIn("log_probabilities", model.predictions)
      self.assertIn("predictions", model.predictions)
      self.assertIsNone(model.loss)
  
imports: import collections
  from absl.testing import parameterized
  from model_search import hparam
  from model_search import loss_fns
  from model_search import task_manager
  from model_search.architecture import architecture_utils
  from model_search.generators import trial_utils
  from model_search.proto import phoenix_spec_pb2
  import numpy
  import tensorflow.compat.v2
  from google.protobuf import text_format
label_desc: Use image node_id 3 for calling the TaskManagerTest obj's underlying member method code with example usage: obj.test_learning_spec_on_predict(learning_rate_spec, not_containing) without return types