code: def __init__(
      self,
      parent,
      vision_kwargs=None,
      qformer_kwargs=None,
      text_kwargs=None,
      is_training=True,
      num_query_tokens=10,
  ):
      if vision_kwargs is None:
          vision_kwargs = {}
      if qformer_kwargs is None:
          qformer_kwargs = {}
      if text_kwargs is None:
          text_kwargs = {}
  
      self.parent = parent
      self.vision_model_tester = InstructBlipVisionModelTester(
          parent, **vision_kwargs
      )
      self.qformer_model_tester = InstructBlipQFormerModelTester(
          parent, **qformer_kwargs
      )
      self.text_model_tester = InstructBlipTextModelDecoderOnlyTester(
          parent, **text_kwargs
      )
      self.is_training = is_training
      self.num_query_tokens = num_query_tokens
  
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 1 to create a new InstructBlipForConditionalGenerationDecoderOnlyModelTester object  with example: obj = InstructBlipForConditionalGenerationDecoderOnlyModelTester(parent, vision_kwargs, qformer_kwargs, text_kwargs, is_training, num_query_tokens)