code: def prepare_config_and_inputs(self):
      input_ids = ids_tensor(
          [self.batch_size, self.seq_length], self.vocab_size
      )
      qformer_input_ids = ids_tensor(
          [self.batch_size, self.seq_length], self.vocab_size
      )
  
      input_mask = None
      if self.use_input_mask:
          input_mask = random_attention_mask(
              [self.batch_size, self.seq_length]
          )
          qformer_attention_mask = ids_tensor(
              [self.batch_size, self.seq_length], vocab_size=2
          )
  
      if input_mask is not None:
          batch_size, seq_length = input_mask.shape
          rnd_start_indices = np.random.randint(
              1, seq_length - 1, size=(batch_size,)
          )
          for batch_idx, start_index in enumerate(rnd_start_indices):
              input_mask[batch_idx, :start_index] = 1
              input_mask[batch_idx, start_index:] = 0
  
      config = self.get_config()
  
      return (
          config,
          input_ids,
          input_mask,
          qformer_input_ids,
          qformer_attention_mask,
      )
  
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 2 for calling the InstructBlipQFormerModelTester obj's underlying member method code with example usage: obj.prepare_config_and_inputs() and returns: config,  input_ids,  input_mask,  qformer_input_ids,  qformer_attention_mask