class_docstr: DatasetBuilder for imagenet2012_multilabel dataset.
docstr: Returns SplitGenerators.
code: def _split_generators(
      self, dl_manager: tfds.download.DownloadManager
  ):
      """Returns SplitGenerators."""
      val_path = os.path.join(
          dl_manager.manual_dir, "ILSVRC2012_img_val.tar"
      )
      if not tf.io.gfile.exists(val_path):
          raise AssertionError(
              "ImageNet requires manual download of the data. Please download "
              "the train and val set and place them into: {}".format(
                  val_path
              )
          )
  
      original_labels = imagenet_common.get_validation_labels(val_path)
  
      (
          multi_labels,
          problematic_images,
          imagenet_m_2022_errors,
      ) = _get_multi_labels_and_problematic_images(dl_manager)
  
      imagenet_m_2022 = dict(
          [(k, multi_labels[k]) for k in imagenet_m_2022_errors]
      )
  
      return {
          "validation": self._generate_examples(
              archive=dl_manager.iter_archive(val_path),
              original_labels=original_labels,
              multi_labels=multi_labels,
              problematic_images=problematic_images,
          ),
          "imagenet_m": self._generate_examples(
              archive=dl_manager.iter_archive(val_path),
              original_labels=original_labels,
              multi_labels=imagenet_m_2022,
              problematic_images=problematic_images,
          ),
      }
  
file_path: datasets/tensorflow_datasets/datasets/imagenet2012_multilabel/imagenet2012_multilabel_dataset_builder.py
imports: import json
  import os
  import numpy
  from tensorflow_datasets.core.utils.lazy_imports_utils import tensorflow
  from tensorflow_datasets.datasets.imagenet2012 import imagenet_common
  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: dict