class_docstr: Randomly generates observations following the given observation_spec.
  
  If an action_spec is provided it validates that the actions used to step the
  environment fall within the defined spec.
code: def render(self, mode: Text = "rgb_array") -> np.ndarray:
      if mode != "rgb_array":
          raise ValueError(
              "Only rendering mode supported is 'rgb_array', got {} instead.".format(
                  mode
              )
          )
  
      return self._rng.randint(
          0, 256, size=self._render_size, dtype=np.uint8
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from typing import Callable, Optional, Sequence, Text
  import numpy
  from tf_agents.environments import py_environment
  from tf_agents.specs import array_spec
  from tf_agents.trajectories import time_step
  from tf_agents.typing import types
  from tf_agents.utils import nest_utils
label_desc: Use image node_id 10 for calling the RandomPyEnvironment obj's underlying member method code with example usage: obj.render(mode) and returns: self