class_docstr: Tiny Imagenet Pytorch Dataset
docstr: Gets the test set image paths
  
  :param class_name: names of the classes of the images to be
      collected.
  :returns img_paths: list of strings (paths)
docstr_tok: ["Gets","the","test","set","image","paths",":","param","class_name",":","names","of","the","classes","of","the","images","to","be","collected",".",":","returns","img_paths",":","list","of","strings","(","paths",")"]
code: def get_test_images_paths(self, class_name) -> List[Path]:
      """
      Gets the test set image paths
  
      :param class_name: names of the classes of the images to be
          collected.
      :returns img_paths: list of strings (paths)
      """
  
      val_img_folder: Path = self.data_folder / "val" / "images"
      annotations_file: Path = (
          self.data_folder / "val" / "val_annotations.txt"
      )
  
      valid_names = []
  
      # filter validation images by class using appropriate file
      with open(str(annotations_file), "r") as f:
          reader = csv.reader(f, dialect="excel-tab")
          for ll in reader:
              if ll[1] == class_name:
                  valid_names.append(ll[0])
  
      img_paths = [val_img_folder / f for f in valid_names]
  
      return img_paths
  
file_path: avalanche/avalanche/benchmarks/datasets/tiny_imagenet/tiny_imagenet.py
imports: import csv
  from pathlib import Path
  from typing import List, Optional, Tuple, Union
  from torchvision.datasets.folder import default_loader
  from torchvision.transforms import ToTensor
  from avalanche.benchmarks.datasets import SimpleDownloadableDataset, default_dataset_location
label_desc: Use image node_id 6 for calling the TinyImagenet obj's underlying member method code with example usage: obj.get_test_images_paths(class_name) and returns: img_paths