code: def test_greedy_xla_generate_simple(self):
      model = TFT5ForConditionalGeneration.from_pretrained("t5-small")
      tokenizer = T5Tokenizer.from_pretrained("t5-small")
  
      # two examples with different lengths to confirm that attention masks are operational in XLA
      sentences = [
          "Translate English to German: Today is a beautiful day.",
          "Translate English to German: I have four cats, three dogs, two birds, and a horse.",
      ]
      input_ids = tokenizer(
          sentences, return_tensors="tf", padding=True
      ).input_ids
  
      xla_generate = tf.function(model.generate, jit_compile=True)
  
      output_ids = model.generate(input_ids)
      output_ids_xla = xla_generate(input_ids)
  
      output_strings = tokenizer.batch_decode(
          output_ids, skip_special_tokens=True
      )
      output_strings_xla = tokenizer.batch_decode(
          output_ids_xla, skip_special_tokens=True
      )
  
      expected_output_string = [
          "Heute ist ein schöner Tag.",
          "Ich habe vier Katzen, drei Hunde, zwei Vögel und ein Pferd.",
      ]
  
      self.assertListEqual(expected_output_string, output_strings)
      self.assertListEqual(expected_output_string, output_strings_xla)
  
imports: from __future__ import annotations
  import unittest
  from transformers import T5Config, is_tf_available
  from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
  from transformers.utils import cached_property
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 1 for calling the TFT5GenerationIntegrationTests obj's underlying member method code with example usage: obj.test_greedy_xla_generate_simple() without return types