class_docstr: Dmlab dataset.
code: def _info(self):
      return tfds.core.DatasetInfo(
          builder=self,
          description=r"""
          The Dmlab dataset contains frames observed by the agent acting in the
          DeepMind Lab environment, which are annotated by the distance between
          the agent and various objects present in the environment. The goal is to
          is to evaluate the ability of a visual model to reason about distances
          from the visual input in 3D environments. The Dmlab dataset consists of
          360x480 color images in 6 classes. The classes are
          {close, far, very far} x {positive reward, negative reward}
          respectively.""",
          features=tfds.features.FeaturesDict(
              {
                  "image": tfds.features.Image(
                      shape=(360, 480, 3), encoding_format="jpeg"
                  ),
                  "filename": tfds.features.Text(),
                  "label": tfds.features.ClassLabel(num_classes=6),
              }
          ),
          homepage="https://github.com/google-research/task_adaptation",
          citation=r"""@article{zhai2019visual,
          title={The Visual Task Adaptation Benchmark},
          author={Xiaohua Zhai and Joan Puigcerver and Alexander Kolesnikov and
                 Pierre Ruyssen and Carlos Riquelme and Mario Lucic and
                 Josip Djolonga and Andre Susano Pinto and Maxim Neumann and
                 Alexey Dosovitskiy and Lucas Beyer and Olivier Bachem and
                 Michael Tschannen and Marcin Michalski and Olivier Bousquet and
                 Sylvain Gelly and Neil Houlsby},
                                year={2019},
                                eprint={1910.04867},
                                archivePrefix={arXiv},
                                primaryClass={cs.CV},
                                url = {https://arxiv.org/abs/1910.04867}
                            }""",
          supervised_keys=("image", "label"),
      )
  
file_path: datasets/tensorflow_datasets/image_classification/dmlab.py
imports: from __future__ import annotations
  import io
  import os
  from absl import logging
  from tensorflow_datasets.core.utils.lazy_imports_utils import tensorflow
  import tensorflow_datasets.public_api
label_desc: Use image node_id 1 for calling the Dmlab obj's underlying member method code with example usage: obj._info() and returns: tfds