class_docstr: Constructs a BLIP image processor.
  
  Args:
      do_resize (`bool`, *optional*, defaults to `True`):
          Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by the
          `do_resize` parameter in the `preprocess` method.
      size (`dict`, *optional*, defaults to `{"height": 384, "width": 384}`):
          Size of the output image after resizing. Can be overridden by the `size` parameter in the `preprocess`
          method.
      resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`):
          Resampling filter to use if resizing the image. Only has an effect if `do_resize` is set to `True`. Can be
          overridden by the `resample` parameter in the `preprocess` method.
      do_rescale (`bool`, *optional*, defaults to `True`):
          Wwhether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the
          `do_rescale` parameter in the `preprocess` method.
      rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
          Scale factor to use if rescaling the image. Only has an effect if `do_rescale` is set to `True`. Can be
          overridden by the `rescale_factor` parameter in the `preprocess` method.
      do_normalize (`bool`, *optional*, defaults to `True`):
          Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess`
          method. Can be overridden by the `do_normalize` parameter in the `preprocess` method.
      image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`):
          Mean to use if normalizing the image. This is a float or list of floats the length of the number of
          channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. Can be
          overridden by the `image_mean` parameter in the `preprocess` method.
      image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`):
          Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
          number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
          Can be overridden by the `image_std` parameter in the `preprocess` method.
      do_convert_rgb (`bool`, *optional*, defaults to `True`):
          Whether to convert the image to RGB.
code: def __init__(
      self,
      do_resize: bool = True,
      size: Dict[str, int] = None,
      resample: PILImageResampling = PILImageResampling.BICUBIC,
      do_rescale: bool = True,
      rescale_factor: Union[int, float] = 1 / 255,
      do_normalize: bool = True,
      image_mean: Optional[Union[float, List[float]]] = None,
      image_std: Optional[Union[float, List[float]]] = None,
      do_convert_rgb: bool = True,
      do_center_crop: bool = True,
      **kwargs,
  ) -> None:
      super().__init__(**kwargs)
      size = size if size is not None else {"height": 224, "width": 224}
      size = get_size_dict(size, default_to_square=True)
  
      self.do_resize = do_resize
      self.size = size
      self.resample = resample
      self.do_rescale = do_rescale
      self.rescale_factor = rescale_factor
      self.do_normalize = do_normalize
      self.image_mean = (
          image_mean if image_mean is not None else OPENAI_CLIP_MEAN
      )
      self.image_std = (
          image_std if image_std is not None else OPENAI_CLIP_STD
      )
      self.do_convert_rgb = do_convert_rgb
      self.do_center_crop = do_center_crop
  
file_path: llmga/llmga/diffusers/src/diffusers/pipelines/blip_diffusion/blip_image_processing.py
imports: from typing import Dict, List, Optional, Union
  import numpy
  import torch
  from transformers.image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
  from transformers.image_transforms import convert_to_rgb, resize, to_channel_dimension_format
  from transformers.image_utils import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD, ChannelDimension, ImageInput, PILImageResampling, infer_channel_dimension_format, is_scaled_image, make_list_of_images, to_numpy_array, valid_images
  from transformers.utils import TensorType, is_vision_available, logging
  from diffusers.utils import numpy_to_pil
label_desc: Use image node_id 1 to create a new BlipImageProcessor object from inherited base classes: BaseImageProcessor with example: obj = BlipImageProcessor(do_resize, size, resample, do_rescale, rescale_factor, do_normalize, image_mean, image_std, do_convert_rgb, do_center_crop)