code: def create_and_check_t5_model(
      self, config, input_ids, input_mask, token_labels
  ):
      model = TFT5Model(config=config)
      inputs = {
          "input_ids": input_ids,
          "decoder_input_ids": input_ids,
          "decoder_attention_mask": input_mask,
      }
      result = model(inputs)
  
      result = model(
          input_ids,
          decoder_attention_mask=input_mask,
          decoder_input_ids=input_ids,
      )
      decoder_output = result.last_hidden_state
      decoder_past = result.past_key_values
      encoder_output = result.encoder_last_hidden_state
      self.parent.assertListEqual(
          list(encoder_output.shape),
          [self.batch_size, self.seq_length, self.hidden_size],
      )
      self.parent.assertListEqual(
          list(decoder_output.shape),
          [self.batch_size, self.seq_length, self.hidden_size],
      )
      # There should be `num_layers` key value embeddings stored in decoder_past[1]
      self.parent.assertEqual(len(decoder_past), config.num_layers)
      # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past[1] tuple
      self.parent.assertEqual(len(decoder_past[0]), 4)
  
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 3 for calling the TFT5ModelTester obj's underlying member method code with example usage: obj.create_and_check_t5_model(config, input_ids, input_mask, token_labels) without return types