class_docstr: Pix3D is a large-scale dataset of diverse image-shape pairs with pixel-level 2D-3D alignment.
class_docstr_tok: ["Pix3D","is","a","large-scale","dataset","of","diverse","image-shape","pairs","with","pixel-level","2D-3D","alignment","."]
code: def _info(self):
      return tfds.core.DatasetInfo(
          builder=self,
          # This is the description that will appear on the datasets page.
          description=_DESCRIPTION,
          # tfds.features.FeatureConnectors
          features=tfds.features.FeaturesDict(
              {
                  "image": tfds_features.Image(
                      shape=(None, None, 3), dtype=tf.uint8
                  ),
                  "image/filename": tfds_features.Text(),
                  "image/source": tfds_features.Text(),
                  "2d_keypoints": tfds_features.FeaturesDict(
                      {
                          "num_annotators": tf.uint8,
                          "num_keypoints": tf.uint8,
                          "keypoints": tfds_features.Tensor(
                              shape=(None,), dtype=tf.float32
                          ),
                      }
                  ),
                  "mask": tfds_features.Image(
                      shape=(None, None, 1), dtype=tf.uint8
                  ),
                  "model": tfg_features.TriangleMesh(),
                  "model/source": tfds_features.Text(),
                  "3d_keypoints": tfds_features.Tensor(
                      shape=(None, 3), dtype=tf.float32
                  ),
                  "voxel": tfg_features.VoxelGrid(
                      shape=(128, 128, 128)
                  ),
                  "pose": tfg_features.Pose(),  # pose of object w.r.t to world.
                  "camera": tfds_features.FeaturesDict(
                      {
                          "parameters": tfg_features.Camera(),
                          "position_with_respect_to_object": tfds_features.Tensor(
                              shape=(3,), dtype=tf.float32
                          ),
                          "inplane_rotation": tf.float32,
                      }
                  ),
                  "category": tfds_features.ClassLabel(
                      num_classes=len(self.CLASS_INDEX)
                  ),
                  "bbox": tfds_features.BBoxFeature(),
                  "truncated": tf.bool,
                  "occluded": tf.bool,
                  "slightly_occluded": tf.bool,
              }
          ),
          # If there's a common (input, target) tuple from the features,
          # specify them here. They'll be used if as_supervised=True in
          # builder.as_dataset.
          supervised_keys=None,
          # Homepage of the dataset for documentation
          homepage="http://pix3d.csail.mit.edu/",
          citation=_CITATION,
      )
  
file_path: graphics/tensorflow_graphics/datasets/pix3d/pix3d.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import json
  import os
  import numpy
  import tensorflow
  from tensorflow_datasets import features
  import tensorflow_datasets.public_api
  from tensorflow_graphics.datasets import features
label_desc: Use image node_id 1 for calling the Pix3d obj's underlying member method code with example usage: obj._info() and returns: tfds