code: def __call__(self, data):
      d = dict(data)
      for key in self.keys:
          size = None
          tile_size = None
  
          if not isinstance(d[key], str):
              image_np = d[key]
          else:
              name = d[key]
              ext = pathlib.Path(name).suffix
              if ext == ".npy":
                  image_np = np.load(d[key])
              else:
                  location = d.get("location", (0, 0))
                  level = d.get("level", 0)
                  size = d.get("size", None)
  
                  # Model input size
                  tile_size = d.get("tile_size", size)
                  if not ext or ext in (
                      ".bif",
                      ".mrxs",
                      ".ndpi",
                      ".scn",
                      ".svs",
                      ".svslide",
                      ".tif",
                      ".tiff",
                      ".vms",
                      ".vmu",
                  ):
                      slide = openslide.OpenSlide(name)
                      size = size if size else slide.dimensions
                      img = slide.read_region(location, level, size)
                  else:
                      img = Image.open(d[key])
                      d["location"] = [0, 0]
                      d["size"] = [0, 0]
  
                  img = img.convert(self.mode) if self.mode else img
                  image_np = np.array(img, dtype=self.dtype)
  
          image_np = np.moveaxis(image_np, 0, 1)
          meta_dict_key = f"{key}_{PostFix.meta()}"
          meta_dict = d.get(meta_dict_key)
          if meta_dict is None:
              d[meta_dict_key] = dict()
              meta_dict = d.get(meta_dict_key)
  
          meta_dict["spatial_shape"] = np.asarray(image_np.shape[:-1])
          meta_dict["original_channel_dim"] = -1
          meta_dict["original_affine"] = None  # type: ignore
          logger.debug(
              f"Image shape: {image_np.shape} vs size: {size} vs tile_size: {tile_size}"
          )
  
          if (
              self.padding
              and tile_size
              and (
                  image_np.shape[0] != tile_size[0]
                  or image_np.shape[1] != tile_size[1]
              )
          ):
              image_np = self.pad_to_shape(image_np, tile_size)
          d[key] = MetaTensor(
              image_np, meta=meta_dict, device=d.get("device")
          )
      return d
  
imports: import logging
  import pathlib
  import numpy
  import openslide
  import torch
  from monai.config import KeysCollection
  from monai.data import MetaTensor
  from monai.transforms import MapTransform
  from monai.utils import PostFix, convert_to_numpy, ensure_tuple
  from PIL import Image
  from scipy.ndimage import binary_fill_holes
  from skimage.morphology import remove_small_holes, remove_small_objects
label_desc: Use image node_id 2 for calling the LoadImagePatchd obj's underlying member method code with example usage: obj.__call__(data) and returns: d