code: def create_and_check_for_conditional_generation(
      self,
      config,
      input_ids,
      attention_mask,
      qformer_input_ids,
      qformer_attention_mask,
      pixel_values,
  ):
      model = (
          InstructBlipForConditionalGeneration(config)
          .to(torch_device)
          .eval()
      )
      with torch.no_grad():
          result = model(
              pixel_values,
              input_ids=input_ids,
              attention_mask=attention_mask,
              qformer_input_ids=qformer_input_ids,
              qformer_attention_mask=qformer_attention_mask,
          )
  
      expected_seq_length = (
          self.num_query_tokens + self.text_model_tester.seq_length
      )
      self.parent.assertEqual(
          result.logits.shape,
          (
              self.vision_model_tester.batch_size,
              expected_seq_length,
              self.text_model_tester.vocab_size,
          ),
      )
  
imports: import inspect
  import tempfile
  import unittest
  import numpy
  import requests
  from transformers import CONFIG_MAPPING, InstructBlipConfig, InstructBlipProcessor, InstructBlipQFormerConfig, InstructBlipVisionConfig
  from transformers.testing_utils import require_accelerate, require_bitsandbytes, require_torch, require_vision, slow, torch_device
  from transformers.utils import is_torch_available, is_vision_available
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
label_desc: Use image node_id 4 for calling the InstructBlipForConditionalGenerationDecoderOnlyModelTester obj's underlying member method code with example usage: obj.create_and_check_for_conditional_generation(config, input_ids, attention_mask, qformer_input_ids, qformer_attention_mask, pixel_values) without return types