code: def test_batch_generation(self):
      model = TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M")
      tokenizer = XGLMTokenizer.from_pretrained("facebook/xglm-564M")
  
      tokenizer.padding_side = "left"
  
      # use different length sentences to test batching
      sentences = [
          "This is an extremelly long sentence that only exists to test the ability of the model to cope with "
          "left-padding, such as in batched generation. The output for the sequence below should be the same "
          "regardless of whether left padding is applied or not. When",
          "Hello, my dog is a little",
      ]
  
      inputs = tokenizer(sentences, return_tensors="tf", padding=True)
      input_ids = inputs["input_ids"]
  
      outputs = model.generate(
          input_ids=input_ids,
          attention_mask=inputs["attention_mask"],
          max_new_tokens=12,
      )
  
      inputs_non_padded = tokenizer(
          sentences[0], return_tensors="tf"
      ).input_ids
      output_non_padded = model.generate(
          input_ids=inputs_non_padded, max_new_tokens=12
      )
  
      inputs_padded = tokenizer(
          sentences[1], return_tensors="tf"
      ).input_ids
      output_padded = model.generate(
          input_ids=inputs_padded, max_new_tokens=12
      )
  
      batch_out_sentence = tokenizer.batch_decode(
          outputs, skip_special_tokens=True
      )
      non_padded_sentence = tokenizer.decode(
          output_non_padded[0], skip_special_tokens=True
      )
      padded_sentence = tokenizer.decode(
          output_padded[0], skip_special_tokens=True
      )
  
      expected_output_sentence = [
          "This is an extremelly long sentence that only exists to test the ability of the model to cope with "
          "left-padding, such as in batched generation. The output for the sequence below should be the same "
          "regardless of whether left padding is applied or not. When left padding is applied, the sequence will be "
          "a single",
          "Hello, my dog is a little bit of a shy one, but he is very friendly",
      ]
      self.assertListEqual(expected_output_sentence, batch_out_sentence)
      self.assertListEqual(
          expected_output_sentence,
          [non_padded_sentence, padded_sentence],
      )
  
imports: from __future__ import annotations
  import unittest
  from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
  from transformers.testing_utils import require_tf, slow
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 3 for calling the TFXGLMModelLanguageGenerationTest obj's underlying member method code with example usage: obj.test_batch_generation() without return types