code: def _get_input_ids_and_config(self, batch_size=2):
      (
          config,
          inputs_dict,
      ) = self.model_tester.prepare_config_and_inputs_for_common()
      config.attention_type = "original_full"
  
      input_ids = inputs_dict[self.input_name]
      attention_mask = torch.ones_like(input_ids, dtype=torch.long)
  
      # cut to half length & take max batch_size 3
      sequence_length = input_ids.shape[-1] // 2
      input_ids = input_ids[:batch_size, :sequence_length]
      attention_mask = attention_mask[:batch_size, :sequence_length]
  
      # generate max 3 tokens
      max_length = input_ids.shape[-1] + 3
      if (
          config.eos_token_id is not None
          and config.pad_token_id is None
      ):
          # hack to allow generate for models such as GPT2 as is done in `generate()`
          config.pad_token_id = config.eos_token_id
      return config, input_ids, attention_mask, max_length
  
imports: import copy
  import tempfile
  import unittest
  from transformers import BigBirdPegasusConfig, is_torch_available
  from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, require_torch_fp16, slow, torch_device
  from ...generation.test_utils import GenerationTesterMixin
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 2 for calling the BigBirdPegasusModelTest obj's underlying member method code with example usage: obj._get_input_ids_and_config(batch_size) and returns: config,  input_ids,  attention_mask,  max_length