code: def test_compile_tf_model(self):
      optimizer = tf.keras.optimizers.Adam(
          learning_rate=3e-5, epsilon=1e-08, clipnorm=1.0
      )
      loss = tf.keras.losses.SparseCategoricalCrossentropy(
          from_logits=True
      )
      metric = tf.keras.metrics.SparseCategoricalAccuracy("accuracy")
  
      for model_class in self.all_model_classes:
          (
              config,
              inputs_dict,
          ) = self.model_tester.prepare_config_and_inputs_for_common(
              return_obj_labels="PreTraining" in model_class.__name__
          )
  
          input_ids = tf.keras.Input(
              batch_shape=(
                  self.model_tester.batch_size,
                  self.model_tester.seq_length,
              ),
              name="input_ids",
              dtype="int32",
          )
          visual_feats = tf.keras.Input(
              batch_shape=(
                  self.model_tester.batch_size,
                  self.model_tester.num_visual_features,
                  self.model_tester.visual_feat_dim,
              ),
              name="visual_feats",
              dtype="int32",
          )
          visual_pos = tf.keras.Input(
              batch_shape=(
                  self.model_tester.batch_size,
                  self.model_tester.num_visual_features,
                  4,
              ),
              name="visual_pos",
              dtype="int32",
          )
  
          # Prepare our model
          model = model_class(config)
  
          # Let's load it from the disk to be sure we can use pretrained weights
          with tempfile.TemporaryDirectory() as tmpdirname:
              outputs = model(
                  self._prepare_for_class(inputs_dict, model_class)
              )  # build the model
              model.save_pretrained(tmpdirname)
              model = model_class.from_pretrained(tmpdirname)
  
          outputs_dict = model(input_ids, visual_feats, visual_pos)
          hidden_states = outputs_dict[0]
  
          # Add a dense layer on top to test integration with other keras modules
          outputs = tf.keras.layers.Dense(
              2, activation="softmax", name="outputs"
          )(hidden_states)
  
          # Compile extended model
          extended_model = tf.keras.Model(
              inputs=[input_ids, visual_feats, visual_pos],
              outputs=[outputs],
          )
          extended_model.compile(
              optimizer=optimizer, loss=loss, metrics=[metric]
          )
  
imports: import os
  import tempfile
  import unittest
  import numpy
  from transformers import LxmertConfig, is_tf_available
  from transformers.testing_utils import require_tf, slow, tooslow
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
label_desc: Use image node_id 10 for calling the TFLxmertModelTest obj's underlying member method code with example usage: obj.test_compile_tf_model() without return types