code: def create_and_check_t5_decoder_model_attention_mask_past(
      self, config, input_ids, decoder_input_ids, attention_mask
  ):
      model = TFT5Model(config=config).get_decoder()
  
      # create attention mask
      half_seq_length = self.seq_length // 2
      attn_mask_begin = tf.ones(
          (self.batch_size, half_seq_length), dtype=tf.int32
      )
      attn_mask_end = tf.zeros(
          (self.batch_size, self.seq_length - half_seq_length),
          dtype=tf.int32,
      )
      attn_mask = tf.concat([attn_mask_begin, attn_mask_end], axis=1)
  
      # first forward pass
      outputs = model(
          input_ids, attention_mask=attn_mask, use_cache=True
      )
  
      # create hypothetical next token and extent to next_input_ids
      next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
  
      # change a random masked slice from input_ids
      random_seq_idx_to_change = (
          ids_tensor((1,), half_seq_length).numpy() + 1
      )
      random_other_next_tokens = ids_tensor(
          (self.batch_size, self.seq_length), config.vocab_size
      )
      vector_condition = tf.range(self.seq_length) == (
          self.seq_length - random_seq_idx_to_change
      )
      condition = tf.transpose(
          tf.broadcast_to(
              tf.expand_dims(vector_condition, -1),
              (self.seq_length, self.batch_size),
          )
      )
      input_ids = tf.where(
          condition, random_other_next_tokens, input_ids
      )
  
      # append to next input_ids and attn_mask
      next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
      attn_mask = tf.concat(
          [attn_mask, tf.ones((attn_mask.shape[0], 1), dtype=tf.int32)],
          axis=1,
      )
  
      # get two different outputs
      output_from_no_past = model(
          next_input_ids, attention_mask=attn_mask
      )[0]
      output_from_past = model(
          next_tokens,
          past_key_values=outputs.past_key_values,
          attention_mask=attn_mask,
      )[0]
  
      # select random slice
      random_slice_idx = (
          ids_tensor((1,), output_from_past.shape[-1]).numpy().item()
      )
      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: 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 6 for calling the TFT5ModelTester obj's underlying member method code with example usage: obj.create_and_check_t5_decoder_model_attention_mask_past(config, input_ids, decoder_input_ids, attention_mask) without return types