code: def read_image_file(
      data_dir: str, image_ext: str, n: int
  ) -> torch.Tensor:
      """Return a Tensor containing the patches"""
  
      def PIL2array(_img: Image.Image) -> np.ndarray:
          """Convert PIL image type to numpy 2D array"""
          return np.array(_img.getdata(), dtype=np.uint8).reshape(
              64, 64
          )
  
      def find_files(_data_dir: str, _image_ext: str) -> List[str]:
          """Return a list with the file names of the images containing the patches"""
          files = []
          # find those files with the specified extension
          for file_dir in os.listdir(_data_dir):
              if file_dir.endswith(_image_ext):
                  files.append(os.path.join(_data_dir, file_dir))
          return sorted(
              files
          )  # sort files in ascend order to keep relations
  
      patches = []
      list_files = find_files(data_dir, image_ext)
  
      for fpath in list_files:
          img = Image.open(fpath)
          for y in range(0, img.height, 64):
              for x in range(0, img.width, 64):
                  patch = img.crop((x, y, x + 64, y + 64))
                  patches.append(PIL2array(patch))
      return torch.ByteTensor(np.array(patches[:n]))
  
file_path: vision/torchvision/datasets/phototour.py
imports: import os
  from typing import Any, Callable, List, Optional, Tuple, Union
  import numpy
  import torch
  from PIL import Image
  from .utils import download_url
  from .vision import VisionDataset
label_desc: Use image node_id 1 for calling a global function with example usage: read_image_file(data_dir, image_ext, n) and returns: torch, np, sorted