code: def create_and_check_causal_lm_model_past_large_inputs(
      self,
      config,
      input_ids,
      token_type_ids,
      input_mask,
      sequence_labels,
      token_labels,
      choice_labels,
  ):
      config.is_decoder = True
  
      model = TFRobertaForCausalLM(config=config)
  
      # special to `RobertaEmbeddings` in `Roberta`:
      #   - its `padding_idx` and its effect on `position_ids`
      #     (TFRobertaEmbeddings.create_position_ids_from_input_ids)
      #   - `1` here is `TFRobertaEmbeddings.padding_idx`
      # avoid `padding_idx` in the past
      input_ids = tf.where(input_ids == 1, 2, input_ids)
  
      input_ids = input_ids[:1, :]
      input_mask = input_mask[:1, :]
      self.batch_size = 1
  
      # first forward pass
      outputs = model(
          input_ids, attention_mask=input_mask, use_cache=True
      )
      past_key_values = outputs.past_key_values
  
      # create hypothetical next token and extent to next_input_ids
      next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
      next_attn_mask = ids_tensor((self.batch_size, 3), 2)
  
      # append to next input_ids and
      next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
      next_attention_mask = tf.concat(
          [input_mask, next_attn_mask], axis=-1
      )
  
      output_from_no_past = model(
          next_input_ids,
          attention_mask=next_attention_mask,
          output_hidden_states=True,
      ).hidden_states[0]
      output_from_past = model(
          next_tokens,
          attention_mask=next_attention_mask,
          past_key_values=past_key_values,
          output_hidden_states=True,
      ).hidden_states[0]
  
      self.parent.assertEqual(
          next_tokens.shape[1], output_from_past.shape[1]
      )
  
      # select random slice
      random_slice_idx = int(
          ids_tensor((1,), output_from_past.shape[-1])
      )
      output_from_no_past_slice = output_from_no_past[
          :, -3:, random_slice_idx
      ]
      output_from_past_slice = output_from_past[:, :, 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 RobertaConfig, is_tf_available
  from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
label_desc: Use image node_id 11 for calling the TFRobertaModelTester obj's underlying member method code with example usage: obj.create_and_check_causal_lm_model_past_large_inputs(config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels) without return types