code: def testDenseFeaturesInKeras(self):
      features = {
          "text": np.array(["hello world", "pair-programming"]),
      }
      label = np.int64([0, 1])
      feature_columns = [
          hub.text_embedding_column_v2(
              "text", self.model, trainable=True
          ),
      ]
      input_features = dict(
          text=keras.layers.Input(
              name="text", shape=[None], dtype=tf.string
          )
      )
      dense_features = keras.layers.DenseFeatures(feature_columns)
      x = dense_features(input_features)
      x = keras.layers.Dense(16, activation="relu")(x)
      logits = keras.layers.Dense(1, activation="linear")(x)
      model = keras.Model(inputs=input_features, outputs=logits)
      model.compile(
          optimizer="rmsprop",
          loss="binary_crossentropy",
          metrics=["accuracy"],
      )
      model.fit(x=features, y=label, epochs=10)
      self.assertAllEqual(model.predict(features["text"]).shape, [2, 1])
  
imports: import logging
  import os
  import numpy
  import tensorflow.compat.v2
  import tensorflow_hub
  from tensorflow.python.feature_column import feature_column_v2
  from tensorflow.python.ops.lookup_ops import HashTable
  from tensorflow.python.ops.lookup_ops import KeyValueTensorInitializer
label_desc: Use image node_id 7 for calling the TextEmbeddingColumnTest obj's underlying member method code with example usage: obj.testDenseFeaturesInKeras() without return types