code: def test_wrong_dict_weight_feature(self, weight_is_a_feature):
      learning_rate_spec = {
          "learning_rate": 0.001,
          "gradient_max_norm": 3,
      }
      spec = phoenix_spec_pb2.PhoenixSpec(
          problem_type=phoenix_spec_pb2.PhoenixSpec.DNN
      )
      text_format.Merge(
          """
          multi_task_spec {
            label_name: "label1"
            number_of_classes: 10
            weight_feature_name: "weight1"
            weight_is_a_feature: %s
          }
          multi_task_spec {
            label_name: "label2"
            number_of_classes: 10
            weight_feature_name: "weight2"
            weight_is_a_feature: %s
          }
      """
          % (str(weight_is_a_feature), str(weight_is_a_feature)),
          spec,
      )
      labels = {
          "label1": tf.ones([20], dtype=tf.int32),
          "label2": tf.ones([20], dtype=tf.int32),
      }
      # Fix the size of the dict labels to bypass the assertion.
      if not weight_is_a_feature:
          labels.update(
              {
                  "not_used": tf.ones([20], dtype=tf.int32),
                  "not_used2": tf.ones([20], dtype=tf.int32),
              }
          )
  
      weights = {
          "weight1": tf.constant([2] * 20),
          "weight2": tf.constant([3] * 20),
      }
      features = {"x": tf.zeros([10, 10])}
      if not weight_is_a_feature:
          features.update(weights)
      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", "previous_model_dir"]
      )
      towers = {"search_generator": [fake_tower(logits_spec, None)]}
  
      with self.assertRaises(KeyError):
          _ = task_manager_instance.create_model_spec(
              features=features,
              params=hp.HParams(optimizer="sgd"),
              learning_rate_spec=learning_rate_spec,
              towers=towers,
              labels=labels,
              trial_mode=trial_utils.TrialMode.NO_PRIOR,
              model_directory=self.get_temp_dir(),
              mode=tf.estimator.ModeKeys.TRAIN,
              my_id=1,
              use_tpu=False,
              predictions_fn=_default_predictions_fn,
          )
  
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 7 for calling the TaskManagerTest obj's underlying member method code with example usage: obj.test_wrong_dict_weight_feature(weight_is_a_feature) without return types