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 are compatible with the given spec.
class_docstr_tok: ["Randomly","generates","observations","following","the","given","observation_spec",".","If","an","action_spec","is","provided",",","it","validates","that","the","actions","used","to","step","the","environment","are","compatible","with","the","given","spec","."]
docstr: Resets the environment and returns the current time_step.
docstr_tok: ["Resets","the","environment","and","returns","the","current","time_step","."]
code: def _reset(self):
      """Resets the environment and returns the current time_step."""
      obs, _ = self._sample_obs_and_reward()
      time_step = ts.restart(
          obs, self._batch_size, reward_spec=self._time_step_spec.reward
      )
      self._update_time_step(time_step)
      return self._current_time_step()
  
file_path: agents/tf_agents/environments/random_tf_environment.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow
  from tf_agents.environments import tf_environment
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import time_step
  from tf_agents.utils import common
  from tf_agents.utils import nest_utils
label_desc: Use image node_id 5 for calling the RandomTFEnvironment obj's underlying member method code with example usage: obj._reset() and returns: self