class_docstr: Constructs an InstructBLIP processor which wraps a BLIP image processor and a LLaMa/T5 tokenizer into a single
  processor.
  
  [`InstructBlipProcessor`] offers all the functionalities of [`BlipImageProcessor`] and [`AutoTokenizer`]. See the
  docstring of [`~BlipProcessor.__call__`] and [`~BlipProcessor.decode`] for more information.
  
  Args:
      image_processor (`BlipImageProcessor`):
          An instance of [`BlipImageProcessor`]. The image processor is a required input.
      tokenizer (`AutoTokenizer`):
          An instance of ['PreTrainedTokenizer`]. The tokenizer is a required input.
      qformer_tokenizer (`AutoTokenizer`):
          An instance of ['PreTrainedTokenizer`]. The Q-Former tokenizer is a required input.
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code: def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
      qformer_tokenizer = AutoTokenizer.from_pretrained(
          pretrained_model_name_or_path, subfolder="qformer_tokenizer"
      )
      args = cls._get_arguments_from_pretrained(
          pretrained_model_name_or_path, **kwargs
      )
      args.append(qformer_tokenizer)
      return cls(*args)
  
file_path: transformers/src/transformers/models/instructblip/processing_instructblip.py
imports: import os
  from typing import List, Optional, Union
  from ...image_processing_utils import BatchFeature
  from ...image_utils import ImageInput
  from ...processing_utils import ProcessorMixin
  from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
  from ...utils import TensorType
  from ..auto import AutoTokenizer
label_desc: Use image node_id 7 for calling the InstructBlipProcessor obj's underlying member method code with example usage: obj.from_pretrained(cls, pretrained_model_name_or_path) and returns: cls