class_docstr: Imagenet Resized dataset.
class_docstr_tok: ["Imagenet","Resized","dataset","."]
code: def _split_generators(self, dl_manager):
      size = self.builder_config.size
  
      if size in [8, 16, 32]:
          train_path, val_path = dl_manager.download(
              [
                  "%s/Imagenet%d_train_npz.zip" % (_URL_PREFIX, size),
                  "%s/Imagenet%d_val_npz.zip" % (_URL_PREFIX, size),
              ]
          )
          train_paths = [train_path]
      elif size == 64:
          # 64x64 uses more than one file due to its size.
          train1_path, train2_path, val_path = dl_manager.download(
              [
                  f"{_URL_PREFIX}/Imagenet64_train_part1_npz.zip",
                  f"{_URL_PREFIX}/Imagenet64_train_part2_npz.zip",
                  f"{_URL_PREFIX}/Imagenet64_val_npz.zip",
              ]
          )
          train_paths = [train1_path, train2_path]
      else:
          raise ValueError("Size not implemented!")
  
      return [
          tfds.core.SplitGenerator(
              name=tfds.Split.TRAIN,
              gen_kwargs={
                  "archive": itertools.chain(
                      *[
                          dl_manager.iter_archive(train_path)
                          for train_path in train_paths
                      ]
                  ),
              },
          ),
          tfds.core.SplitGenerator(
              name=tfds.Split.VALIDATION,
              gen_kwargs={
                  "archive": dl_manager.iter_archive(val_path),
              },
          ),
      ]
  
file_path: datasets/tensorflow_datasets/datasets/imagenet_resized/imagenet_resized_dataset_builder.py
imports: import io
  import itertools
  import numpy
  import tensorflow_datasets.public_api
label_desc: Use image node_id 2 for calling the Builder obj's underlying member method code with example usage: obj._split_generators(dl_manager) and returns: list