class_docstr: TFDS definition for ShapenetPretraining dataset
class_docstr_tok: ["TFDS","definition","for","ShapenetPretraining","dataset"]
docstr: Returns the dataset metadata.
docstr_tok: ["Returns","the","dataset","metadata","."]
code: def _info(self) -> tfds.core.DatasetInfo:
      """Returns the dataset metadata."""
  
      return tfds.core.DatasetInfo(
          builder=self,
          description=_DESCRIPTION,
          features=tfds.features.FeaturesDict(
              {
                  "image_id": tfds.features.Text(),
                  "asset_id": tfds.features.Text(),
                  "label": tfds.features.ClassLabel(
                      names=[
                          "airplane",
                          "ashcan",
                          "bag",
                          "basket",
                          "bathtub",
                          "bed",
                          "bench",
                          "birdhouse",
                          "bookshelf",
                          "bottle",
                          "bowl",
                          "bus",
                          "cabinet",
                          "camera",
                          "can",
                          "cap",
                          "car",
                          "cellular telephone",
                          "chair",
                          "clock",
                          "computer keyboard",
                          "dishwasher",
                          "display",
                          "earphone",
                          "faucet",
                          "file",
                          "guitar",
                          "helmet",
                          "jar",
                          "knife",
                          "lamp",
                          "laptop",
                          "loudspeaker",
                          "mailbox",
                          "microphone",
                          "microwave",
                          "motorcycle",
                          "mug",
                          "piano",
                          "pillow",
                          "pistol",
                          "pot",
                          "printer",
                          "remote control",
                          "rifle",
                          "rocket",
                          "skateboard",
                          "sofa",
                          "stove",
                          "table",
                          "telephone",
                          "tower",
                          "train",
                          "vessel",
                          "washer",
                      ]
                  ),
                  "camera_position": tfds.features.Tensor(
                      shape=(3,), dtype=tf.float32
                  ),
                  "image": tfds.features.Image(shape=(None, None, 4)),
              }
          ),
          supervised_keys=("image", "label"),
          homepage="https://github.com/google-research/kubric",
          citation=_CITATION,
      )
  
file_path: kubric/challenges/pretraining_visual/shapenet_pretraining.py
imports: import itertools
  import json
  import logging
  import numpy
  import png
  import tensorflow
  import tensorflow_datasets.public_api
  from typing import List
label_desc: Use image node_id 1 for calling the ShapenetPretraining obj's underlying member method code with example usage: obj._info() and returns: tfds