class_docstr: An `EnvProblem` when observations are RGB arrays.
  
  This takes care of wrapping a rendered gym environment to behave like a
  `VideoProblem`. This class assumes the underlying gym environment is either a
  `gym_utils.RenderedEnv` or it natively returns rendered scene for
  observations. i.e. the underlying gym environment should have a
  `Box` observation space with the following shape: [frame_height, frame_width,
  channels]
  
  Note: The method resolution order for this class is:
  `RenderedEnvProblem`, `EnvProblem`, `Env`, `VideoProblem`, `Problem`
docstr: Return a mix of env and video data fields and decoders.
docstr_tok: ["Return","a","mix","of","env","and","video","data","fields","and","decoders","."]
code: def example_reading_spec(self):
      """Return a mix of env and video data fields and decoders."""
      slim = contrib.slim()
      (
          video_fields,
          video_decoders,
      ) = video_utils.VideoProblem.example_reading_spec(self)
      (
          env_fields,
          env_decoders,
      ) = gym_env_problem.GymEnvProblem.example_reading_spec(self)
  
      # Remove raw observations field since we want to capture them as videos.
      env_fields.pop(env_problem.OBSERVATION_FIELD)
      env_decoders.pop(env_problem.OBSERVATION_FIELD)
  
      # Add frame number spec and decoder.
      env_fields[_FRAME_NUMBER_FIELD] = tf.FixedLenFeature(
          (1,), tf.int64
      )
      env_decoders[_FRAME_NUMBER_FIELD] = slim.tfexample_decoder.Tensor(
          _FRAME_NUMBER_FIELD
      )
  
      # Add video fields and decoders
      env_fields.update(video_fields)
      env_decoders.update(video_decoders)
      return env_fields, env_decoders
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
  import png
  import six
  from tensor2tensor.data_generators import video_utils
  from tensor2tensor.envs import env_problem
  from tensor2tensor.envs import gym_env_problem
  from tensor2tensor.utils import contrib
  import tensorflow.compat.v1
label_desc: Use image node_id 3 for calling the RenderedEnvProblem obj's underlying member method code with example usage: obj.example_reading_spec() and returns: env_fields,  env_decoders