class_docstr: This is an image processor mixin used to provide saving/loading functionality for sequential and image feature
  extractors.
docstr: Instantiates a type of [`~image_processing_utils.ImageProcessingMixin`] from a Python dictionary of parameters.
  
  Args:
      image_processor_dict (`Dict[str, Any]`):
          Dictionary that will be used to instantiate the image processor object. Such a dictionary can be
          retrieved from a pretrained checkpoint by leveraging the
          [`~image_processing_utils.ImageProcessingMixin.to_dict`] method.
      kwargs (`Dict[str, Any]`):
          Additional parameters from which to initialize the image processor object.
  
  Returns:
      [`~image_processing_utils.ImageProcessingMixin`]: The image processor object instantiated from those
      parameters.
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code: def from_dict(cls, image_processor_dict: Dict[str, Any], **kwargs):
      """
      Instantiates a type of [`~image_processing_utils.ImageProcessingMixin`] from a Python dictionary of parameters.
  
      Args:
          image_processor_dict (`Dict[str, Any]`):
              Dictionary that will be used to instantiate the image processor object. Such a dictionary can be
              retrieved from a pretrained checkpoint by leveraging the
              [`~image_processing_utils.ImageProcessingMixin.to_dict`] method.
          kwargs (`Dict[str, Any]`):
              Additional parameters from which to initialize the image processor object.
  
      Returns:
          [`~image_processing_utils.ImageProcessingMixin`]: The image processor object instantiated from those
          parameters.
      """
      image_processor_dict = image_processor_dict.copy()
      return_unused_kwargs = kwargs.pop("return_unused_kwargs", False)
  
      # The `size` parameter is a dict and was previously an int or tuple in feature extractors.
      # We set `size` here directly to the `image_processor_dict` so that it is converted to the appropriate
      # dict within the image processor and isn't overwritten if `size` is passed in as a kwarg.
      if "size" in kwargs and "size" in image_processor_dict:
          image_processor_dict["size"] = kwargs.pop("size")
      if "crop_size" in kwargs and "crop_size" in image_processor_dict:
          image_processor_dict["crop_size"] = kwargs.pop("crop_size")
  
      image_processor = cls(**image_processor_dict)
  
      # Update image_processor with kwargs if needed
      to_remove = []
      for key, value in kwargs.items():
          if hasattr(image_processor, key):
              setattr(image_processor, key, value)
              to_remove.append(key)
      for key in to_remove:
          kwargs.pop(key, None)
  
      logger.info(f"Image processor {image_processor}")
      if return_unused_kwargs:
          return image_processor, kwargs
      else:
          return image_processor
  
imports: import copy
  import json
  import os
  import warnings
  from io import BytesIO
  from typing import Any, Dict, Iterable, List, Optional, Tuple, Union
  import numpy
  import requests
  from .dynamic_module_utils import custom_object_save
  from .feature_extraction_utils import BatchFeature
  from .image_transforms import center_crop, normalize, rescale
  from .image_utils import ChannelDimension
  from .utils import IMAGE_PROCESSOR_NAME, PushToHubMixin, add_model_info_to_auto_map, cached_file, copy_func, download_url, is_offline_mode, is_remote_url, is_vision_available, logging
label_desc: Use image node_id 6 for calling the ImageProcessingMixin obj's underlying member method code with example usage: obj.from_dict(cls, image_processor_dict) and returns: image_processor,  kwargs, image_processor