class_docstr: Constructs a PoolFormer 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
          `do_resize` in the `preprocess` method.
      size (`Dict[str, int]` *optional*, defaults to `{"shortest_edge": 224}`):
          Size of the image after resizing. Can be overridden by `size` in the `preprocess` method. If crop_pct is
          unset:
          - size is `{"height": h, "width": w}`: the image is resized to `(h, w)`.
          - size is `{"shortest_edge": s}`: the shortest edge of the image is resized to s whilst maintaining the
            aspect ratio.
  
          If crop_pct is set:
          - size is `{"height": h, "width": w}`: the image is resized to `(int(floor(h/crop_pct)),
            int(floor(w/crop_pct)))`
          - size is `{"height": c, "width": c}`: the shortest edge of the image is resized to `int(floor(c/crop_pct)`
            whilst maintaining the aspect ratio.
          - size is `{"shortest_edge": c}`: the shortest edge of the image is resized to `int(floor(c/crop_pct)`
            whilst maintaining the aspect ratio.
      crop_pct (`float`, *optional*, defaults to 0.9):
          Percentage of the image to crop from the center. Can be overridden by `crop_pct` in the `preprocess`
          method.
      resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`):
          Resampling filter to use if resizing the image. Can be overridden by `resample` in the `preprocess` method.
      do_center_crop (`bool`, *optional*, defaults to `True`):
          Whether to center crop the image. If the input size is smaller than `crop_size` along any edge, the image
          is padded with 0's and then center cropped. Can be overridden by `do_center_crop` in the `preprocess`
          method.
      crop_size (`Dict[str, int]`, *optional*, defaults to `{"height": 224, "width": 224}`):
          Size of the image after applying center crop. Only has an effect if `do_center_crop` is set to `True`. Can
          be overridden by the `crop_size` parameter in the `preprocess` method.
      rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
          Scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter in the
          `preprocess` method.
      do_rescale (`bool`, *optional*, defaults to `True`):
          Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale`
          parameter in the `preprocess` method.
      do_normalize (`bool`, *optional*, defaults to `True`):
          Controls whether to normalize the image. 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.
      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.
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docstr: Preprocess an image or batch of images.
  
  Args:
      images (`ImageInput`):
          Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
          passing in images with pixel values between 0 and 1, set `do_rescale=False`.
      do_resize (`bool`, *optional*, defaults to `self.do_resize`):
          Whether to resize the image.
      size (`Dict[str, int]`, *optional*, defaults to `self.size`):
          Size of the image after applying resize.
      crop_pct (`float`, *optional*, defaults to `self.crop_pct`):
          Percentage of the image to crop. Only has an effect if `do_resize` is set to `True`.
      resample (`int`, *optional*, defaults to `self.resample`):
          Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`, Only
          has an effect if `do_resize` is set to `True`.
      do_center_crop (`bool`, *optional*, defaults to `self.do_center_crop`):
          Whether to center crop the image.
      crop_size (`Dict[str, int]`, *optional*, defaults to `self.crop_size`):
          Size of the image after applying center crop.
      do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
          Whether to rescale the image values between [0 - 1].
      rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
          Rescale factor to rescale the image by if `do_rescale` is set to `True`.
      do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
          Whether to normalize the image.
      image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
          Image mean.
      image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
          Image standard deviation.
      return_tensors (`str` or `TensorType`, *optional*):
          The type of tensors to return. Can be one of:
              - Unset: Return a list of `np.ndarray`.
              - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
              - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
              - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
              - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
      data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
          The channel dimension format for the output image. Can be one of:
              - `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
              - `ChannelDimension.LAST`: image in (height, width, num_channels) format.
      input_data_format (`ChannelDimension` or `str`, *optional*):
          The channel dimension format for the input image. If unset, the channel dimension format is inferred
          from the input image. Can be one of:
          - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
          - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
          - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
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code: def preprocess(
      self,
      images: ImageInput,
      do_resize: bool = None,
      size: Dict[str, int] = None,
      crop_pct: int = None,
      resample: PILImageResampling = None,
      do_center_crop: bool = None,
      crop_size: Dict[str, int] = None,
      do_rescale: bool = None,
      rescale_factor: float = None,
      do_normalize: bool = None,
      image_mean: Optional[Union[float, List[float]]] = None,
      image_std: Optional[Union[float, List[float]]] = None,
      return_tensors: Optional[Union[str, TensorType]] = None,
      data_format: ChannelDimension = ChannelDimension.FIRST,
      input_data_format: Optional[Union[str, ChannelDimension]] = None,
      **kwargs,
  ) -> PIL.Image.Image:
      """
      Preprocess an image or batch of images.
  
      Args:
          images (`ImageInput`):
              Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
              passing in images with pixel values between 0 and 1, set `do_rescale=False`.
          do_resize (`bool`, *optional*, defaults to `self.do_resize`):
              Whether to resize the image.
          size (`Dict[str, int]`, *optional*, defaults to `self.size`):
              Size of the image after applying resize.
          crop_pct (`float`, *optional*, defaults to `self.crop_pct`):
              Percentage of the image to crop. Only has an effect if `do_resize` is set to `True`.
          resample (`int`, *optional*, defaults to `self.resample`):
              Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`, Only
              has an effect if `do_resize` is set to `True`.
          do_center_crop (`bool`, *optional*, defaults to `self.do_center_crop`):
              Whether to center crop the image.
          crop_size (`Dict[str, int]`, *optional*, defaults to `self.crop_size`):
              Size of the image after applying center crop.
          do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
              Whether to rescale the image values between [0 - 1].
          rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
              Rescale factor to rescale the image by if `do_rescale` is set to `True`.
          do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
              Whether to normalize the image.
          image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
              Image mean.
          image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
              Image standard deviation.
          return_tensors (`str` or `TensorType`, *optional*):
              The type of tensors to return. Can be one of:
                  - Unset: Return a list of `np.ndarray`.
                  - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
                  - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
                  - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
                  - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
          data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
              The channel dimension format for the output image. Can be one of:
                  - `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
                  - `ChannelDimension.LAST`: image in (height, width, num_channels) format.
          input_data_format (`ChannelDimension` or `str`, *optional*):
              The channel dimension format for the input image. If unset, the channel dimension format is inferred
              from the input image. Can be one of:
              - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
              - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
              - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
      """
      do_resize = do_resize if do_resize is not None else self.do_resize
      crop_pct = crop_pct if crop_pct is not None else self.crop_pct
      resample = resample if resample is not None else self.resample
      do_center_crop = (
          do_center_crop
          if do_center_crop is not None
          else self.do_center_crop
      )
      do_rescale = (
          do_rescale if do_rescale is not None else self.do_rescale
      )
      rescale_factor = (
          rescale_factor
          if rescale_factor is not None
          else self.rescale_factor
      )
      do_normalize = (
          do_normalize
          if do_normalize is not None
          else self.do_normalize
      )
      image_mean = (
          image_mean if image_mean is not None else self.image_mean
      )
      image_std = image_std if image_std is not None else self.image_std
  
      size = size if size is not None else self.size
      size = get_size_dict(size, default_to_square=False)
      crop_size = crop_size if crop_size is not None else self.crop_size
      crop_size = get_size_dict(crop_size, param_name="crop_size")
  
      images = make_list_of_images(images)
  
      if not valid_images(images):
          raise ValueError(
              "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
              "torch.Tensor, tf.Tensor or jax.ndarray."
          )
  
      if do_resize and size is None or resample is None:
          raise ValueError(
              "Size and resample must be specified if do_resize is True."
          )
  
      if do_center_crop and crop_pct is None:
          raise ValueError(
              "Crop_pct must be specified if do_center_crop is True."
          )
  
      if do_rescale and rescale_factor is None:
          raise ValueError(
              "Rescale factor must be specified if do_rescale is True."
          )
  
      if do_normalize and (image_mean is None or image_std is None):
          raise ValueError(
              "Image mean and std must be specified if do_normalize is True."
          )
  
      # All transformations expect numpy arrays.
      images = [to_numpy_array(image) for image in images]
  
      if is_scaled_image(images[0]) and do_rescale:
          logger.warning_once(
              "It looks like you are trying to rescale already rescaled images. If the input"
              " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
          )
  
      if input_data_format is None:
          # We assume that all images have the same channel dimension format.
          input_data_format = infer_channel_dimension_format(images[0])
  
      if do_resize:
          images = [
              self.resize(
                  image=image,
                  size=size,
                  crop_pct=crop_pct,
                  resample=resample,
                  input_data_format=input_data_format,
              )
              for image in images
          ]
  
      if do_center_crop:
          images = [
              self.center_crop(
                  image=image,
                  size=crop_size,
                  input_data_format=input_data_format,
              )
              for image in images
          ]
  
      if do_rescale:
          images = [
              self.rescale(
                  image=image,
                  scale=rescale_factor,
                  input_data_format=input_data_format,
              )
              for image in images
          ]
  
      if do_normalize:
          images = [
              self.normalize(
                  image=image,
                  mean=image_mean,
                  std=image_std,
                  input_data_format=input_data_format,
              )
              for image in images
          ]
  
      images = [
          to_channel_dimension_format(
              image, data_format, input_channel_dim=input_data_format
          )
          for image in images
      ]
  
      data = {"pixel_values": images}
      return BatchFeature(data=data, tensor_type=return_tensors)
  
file_path: transformers/src/transformers/models/poolformer/image_processing_poolformer.py
imports: from typing import Dict, List, Optional, Union
  import numpy
  from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
  from ...image_transforms import get_resize_output_image_size, resize, to_channel_dimension_format
  from ...image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, ChannelDimension, ImageInput, PILImageResampling, infer_channel_dimension_format, is_scaled_image, make_list_of_images, to_numpy_array, valid_images
  from ...utils import TensorType, is_vision_available, logging
label_desc: Use image node_id 3 for calling the PoolFormerImageProcessor obj's underlying member method code with example usage: obj.preprocess(images, do_resize, size, crop_pct, resample, do_center_crop, crop_size, do_rescale, rescale_factor, do_normalize, image_mean, image_std, return_tensors, data_format, input_data_format) and returns: BatchFeature