code: def _feature_description(task_name: str) -> Dict[str, Any]:
      """Returns the shape of a tf.Example for the given task."""
      if "humanoid" in task_name:
          return {
              "observation/walker/joints_vel": _sequence(56),
              "observation/walker/sensors_velocimeter": _sequence(3),
              "observation/walker/sensors_gyro": _sequence(3),
              "observation/walker/joints_pos": _sequence(56),
              "observation/walker/world_zaxis": _sequence(3),
              "observation/walker/body_height": _sequence(1),
              "observation/walker/sensors_accelerometer": _sequence(3),
              "observation/walker/end_effectors_pos": _sequence(12),
              "observation/walker/egocentric_camera": _sequence(
                  shape_size=None, dtype=tf.string
              ),
              "action": _sequence(56),
              "discount": _sequence(),
              "reward": _sequence(),
              "step_type": _sequence(),
              "episode_id": tf.io.FixedLenFeature([], tf.int64),
              "timestamp": tf.io.FixedLenFeature([], tf.int64),
          }
      else:
          return {
              "observation/walker/joints_pos": _sequence(30),
              "observation/walker/joints_vel": _sequence(30),
              "observation/walker/tendons_pos": _sequence(8),
              "observation/walker/tendons_vel": _sequence(8),
              "observation/walker/appendages_pos": _sequence(15),
              "observation/walker/world_zaxis": _sequence(3),
              "observation/walker/sensors_accelerometer": _sequence(3),
              "observation/walker/sensors_velocimeter": _sequence(3),
              "observation/walker/sensors_gyro": _sequence(3),
              "observation/walker/sensors_touch": _sequence(4),
              "observation/walker/egocentric_camera": _sequence(
                  shape_size=None, dtype=tf.string
              ),
              "action": _sequence(38),
              "discount": _sequence(),
              "reward": _sequence(),
              "step_type": _sequence(),
              "episode_id": tf.io.FixedLenFeature([], tf.int64),
              "timestamp": tf.io.FixedLenFeature([], tf.int64),
          }
  
file_path: datasets/tensorflow_datasets/rl_unplugged/rlu_locomotion/rlu_locomotion.py
imports: from __future__ import annotations
  from typing import Any, Dict, Optional
  import numpy
  from tensorflow_datasets.core.utils.lazy_imports_utils import tensorflow
  import tensorflow_datasets.public_api
  from tensorflow_datasets.rl_unplugged import rlu_common
label_desc: Use image node_id 2 for calling a global function with example usage: _feature_description(task_name) and returns: dict, dict