code: def test_generate_tf_function_export(self):
      test_model = TFAutoModelForCausalLM.from_pretrained(
          "hf-internal-testing/tiny-random-gpt2"
      )
      max_length = 2
  
      class DummyModel(tf.Module):
          def __init__(self, model):
              super(DummyModel, self).__init__()
              self.model = model
  
          @tf.function(
              input_signature=(
                  tf.TensorSpec(
                      (None, max_length), tf.int32, name="input_ids"
                  ),
                  tf.TensorSpec(
                      (None, max_length),
                      tf.int32,
                      name="attention_mask",
                  ),
              ),
              jit_compile=True,
          )
          def serving(self, input_ids, attention_mask):
              outputs = self.model.generate(
                  input_ids=input_ids,
                  attention_mask=attention_mask,
                  max_new_tokens=max_length,
                  return_dict_in_generate=True,
              )
              return {"sequences": outputs["sequences"]}
  
      dummy_input_ids = [[2, 0], [102, 103]]
      dummy_attention_masks = [[1, 0], [1, 1]]
      dummy_model = DummyModel(model=test_model)
      with tempfile.TemporaryDirectory() as tmp_dir:
          tf.saved_model.save(
              dummy_model,
              tmp_dir,
              signatures={"serving_default": dummy_model.serving},
          )
          serving_func = tf.saved_model.load(tmp_dir).signatures[
              "serving_default"
          ]
          for batch_size in range(1, len(dummy_input_ids) + 1):
              inputs = {
                  "input_ids": tf.constant(
                      dummy_input_ids[:batch_size]
                  ),
                  "attention_mask": tf.constant(
                      dummy_attention_masks[:batch_size]
                  ),
              }
              tf_func_outputs = serving_func(**inputs)["sequences"]
              tf_model_outputs = test_model.generate(
                  **inputs, max_new_tokens=max_length
              )
              tf.debugging.assert_equal(
                  tf_func_outputs, tf_model_outputs
              )
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/tests/generation/test_generation_tf_utils.py
imports: import tempfile
  import unittest
  from transformers import is_tf_available
  from transformers.testing_utils import require_tf, slow
label_desc: Use image node_id 1 for calling the TFGenerationIntegrationTests obj's underlying member method code with example usage: obj.test_generate_tf_function_export() and returns: dict