class_docstr: Convert an input PIL image to Patches and Image
  This transform was proposed in PIRL - https://arxiv.org/abs/1912.01991.
  
  Input:
      PIL Image
  Returns:
      list containing N+1 elements
          + zeroth element: a RandomResizedCrop of the image
          + remainder: N patches extracted uniformly from a RandomResizedCrop
docstr: Instantiates ImgPilToPatchesAndImage from configuration.
  
  Args:
      config (Dict): arguments for for the transform
  
  Returns:
      ImgPilToPatchesAndImage instance.
code: def from_config(
      cls, config: Dict[str, Any]
  ) -> "ImgPilToPatchesAndImage":
      """
      Instantiates ImgPilToPatchesAndImage from configuration.
  
      Args:
          config (Dict): arguments for for the transform
  
      Returns:
          ImgPilToPatchesAndImage instance.
      """
      return cls(**config)
  
file_path: vissl/vissl/data/ssl_transforms/img_pil_to_patches_and_image.py
imports: from typing import Any, Dict
  import numpy
  import PIL
  import torchvision.transforms
  from classy_vision.dataset.transforms import register_transform
  from classy_vision.dataset.transforms.classy_transform import ClassyTransform
  from vissl.data.ssl_transforms.img_patches_tensor import ImgPatchesFromTensor
label_desc: Use image node_id 3 for calling the ImgPilToPatchesAndImage obj's underlying member method code with example usage: obj.from_config(cls, config) and returns: cls