code: def setUp(self):
      super().setUp()
  
      self._context_feature_spec = {
          "context_1": tf.io.RaggedFeature(dtype=tf.int64)
      }
      self._example_feature_spec = {
          name: tf.io.FixedLenFeature(
              shape=(1,), dtype=tf.float32, default_value=0.0
          )
          for name in ["feature_1", "feature_2"]
      }
  
      def context_embedding(x):
          return tf.reduce_mean(
              tf.keras.layers.Embedding(input_dim=10, output_dim=4)(x),
              axis=-2,
          )
  
      preprocess_spec = {
          "context_1": context_embedding,
          "feature_1": tf.keras.layers.experimental.preprocessing.Normalization(
              axis=-1
          ),
          "feature_2": lambda t: tf.math.log1p(t * tf.sign(t))
          * tf.sign(t),
      }
  
      self._input_creator = model_lib.FeatureSpecInputCreator(
          self._context_feature_spec, self._example_feature_spec
      )
      self._preprocessor = model_lib.PreprocessorWithSpec(
          preprocess_spec
      )
  
imports: from absl.testing import parameterized
  import tensorflow
  from google.protobuf import text_format
  from tensorflow_ranking.python import data
  from tensorflow_ranking.python.keras import feature
  from tensorflow_ranking.python.keras import losses
  from tensorflow_ranking.python.keras import metrics
  from tensorflow_ranking.python.keras import model
  from tensorflow_ranking.python.keras import network
  from tensorflow_serving.apis import input_pb2
label_desc: Use image node_id 1 for calling the ModelBuilderTest obj's underlying member method code with example usage: obj.setUp() and returns: tf