code: def create_and_check_t5_decoder_model_past(
      self, config, input_ids, decoder_input_ids, attention_mask
  ):
      model = TFT5Model(config=config).get_decoder()
  
      input_ids = input_ids[:1, :]
      self.batch_size = 1
  
      # first forward pass
      outputs = model(input_ids, use_cache=True)
  
      outputs_use_cache_conf = model(input_ids)
      outputs_no_past = model(input_ids, use_cache=False)
  
      self.parent.assertTrue(
          len(outputs) == len(outputs_use_cache_conf)
      )
      self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)
  
      # create hypothetical next token and extent to next_input_ids
      next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
  
      # append to next input_ids and
      next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
  
      output_from_no_past = model(next_input_ids)[0]
      output_from_past = model(
          next_tokens, past_key_values=outputs.past_key_values
      )[0]
  
      # select random slice
      random_slice_idx = int(
          ids_tensor((1,), output_from_past.shape[-1])
      )
      output_from_no_past_slice = output_from_no_past[
          :, -1, random_slice_idx
      ]
      output_from_past_slice = output_from_past[:, 0, random_slice_idx]
  
      # test that outputs are equal for slice
      tf.debugging.assert_near(
          output_from_past_slice, output_from_no_past_slice, rtol=1e-3
      )
  
imports: import unittest
  from transformers import T5Config, is_tf_available
  from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow, tooslow
  from transformers.utils import cached_property
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
label_desc: Use image node_id 5 for calling the TFT5ModelTester obj's underlying member method code with example usage: obj.create_and_check_t5_decoder_model_past(config, input_ids, decoder_input_ids, attention_mask) without return types