code: def _name_to_features(config_name: str):
      if config_name == "rlds":
          return tfds.features.FeaturesDict(
              {
                  "episode_id": np.str_,
                  "skill": np.uint8,
                  "steps": tfds.features.Dataset(_steps_features()),
                  "task_code": np.str_,
              }
          )
      return tfds.features.FeaturesDict(
          {
              "image_0": tfds.features.Image(
                  shape=(512, 640, 3), dtype=np.uint8
              ),
              "image_1": tfds.features.Image(
                  shape=(480, 640, 3), dtype=np.uint8
              ),
              "image_2": tfds.features.Image(
                  shape=(480, 640, 3), dtype=np.uint8
              ),
              "success": np.bool_,
              "task_code": np.str_,
          }
      )
  
file_path: datasets/tensorflow_datasets/robotics/mt_opt/mt_opt.py
imports: from __future__ import annotations
  import os
  from typing import Any, Dict, Generator, Tuple
  import numpy
  from tensorflow_datasets.core.utils.lazy_imports_utils import tensorflow
  import tensorflow_datasets.public_api
label_desc: Use image node_id 2 for calling a global function with example usage: _name_to_features(config_name) and returns: tfds, tfds