class_docstr: Tiny Imagenet Pytorch Dataset
docstr: Load all images paths and targets.
  
  :return: train_set, test_set: (train_X_paths, train_y).
docstr_tok: ["Load","all","images","paths","and","targets",".",":","return",":","train_set",",","test_set",":","(","train_X_paths",",","train_y",")","."]
code: def load_data(self):
      """
      Load all images paths and targets.
  
      :return: train_set, test_set: (train_X_paths, train_y).
      """
  
      data: Tuple[List[Path], List[int]] = ([], [])
  
      classes = list(range(200))
      for class_id in classes:
          class_name = self.id2label[class_id]
  
          if self.train:
              X = self.get_train_images_paths(class_name)
              Y = [class_id] * len(X)
          else:
              # test set
              X = self.get_test_images_paths(class_name)
              Y = [class_id] * len(X)
  
          data[0].extend(X)
          data[1].extend(Y)
  
      return data
  
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 4 for calling the TinyImagenet obj's underlying member method code with example usage: obj.load_data() and returns: data