code: def testLoadingDifferentFeatureColumnsFails(self):
      features = [
          np.array(["hello world", "pair-programming"]),
      ]
      label = np.int64([0, 1])
      feature_columns = [
          hub.text_embedding_column_v2(
              "text_1", self.model, trainable=True
          ),
      ]
      # Build the first model.
      input_features = dict(
          text_1=keras.layers.Input(
              name="text_1", 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_1 = keras.Model(inputs=input_features, outputs=logits)
      model_1.compile(
          optimizer="rmsprop",
          loss="binary_crossentropy",
          metrics=["accuracy"],
      )
      model_1.fit(x=features, y=label, epochs=10)
  
      checkpoint_path = os.path.join(
          self.get_temp_dir(), "checkpoints", "checkpoint-1"
      )
      model_1.save_weights(checkpoint_path)
  
      # Build the second model with feature columns that have different names.
      feature_columns = [
          hub.text_embedding_column_v2(
              "text_2", self.model, trainable=True
          ),
      ]
      input_features = dict(
          text_2=keras.layers.Input(
              name="text_2", 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_2 = keras.Model(inputs=input_features, outputs=logits)
      model_2.compile(
          optimizer="rmsprop",
          loss="binary_crossentropy",
          metrics=["accuracy"],
      )
  
      # Loading of checkpoints from the first model into the second model should
      # fail.
      with self.assertRaisesRegexp(
          AssertionError, ".*not bound to checkpointed values.*"
      ):
          model_2.load_weights(checkpoint_path).assert_consumed()
  
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 8 for calling the TextEmbeddingColumnTest obj's underlying member method code with example usage: obj.testLoadingDifferentFeatureColumnsFails() without return types