class_docstr: A single TimeStep in an environment.
  
  A :class:`~TimeStep` represents a single sample when an agent interacts
      with an environment. It describes as SARS (State–action–reward–state)
      tuple that characterizes the evolution of a MDP.
  
  Attributes:
      env_spec (EnvSpec): Specification for the environment from which this
          data was sampled.
      episode_info (dict[str, np.ndarray]): A dict of numpy arrays of shape
          :math:`(S*^,)` containing episode-level information of each
          episode.  For example, in goal-conditioned reinforcement learning
          this could contain the goal state for each episode.
      observation (numpy.ndarray): A numpy array of shape :math:`(O^*)`
          containing the observation for this time step in the
          environment. These must conform to
          :obj:`EnvStep.observation_space`.
          The observation before applying the action.
          `None` if `step_type` is `StepType.FIRST`, i.e. at the start of a
          sequence.
      action (numpy.ndarray): A numpy array of shape :math:`(A^*)`
          containing the action for this time step. These must conform
          to :obj:`EnvStep.action_space`.
          `None` if `step_type` is `StepType.FIRST`, i.e. at the start of a
          sequence.
      reward (float): A float representing the reward for taking the action
          given the observation, at this time step.
          `None` if `step_type` is `StepType.FIRST`, i.e. at the start of a
          sequence.
      next_observation (numpy.ndarray): A numpy array of shape :math:`(O^*)`
          containing the observation for this time step in the
          environment. These must conform to
          :obj:`EnvStep.observation_space`.
          The observation after applying the action.
      env_info (dict): A dict arbitrary environment state information.
      agent_info (dict): A dict of arbitrary agent
          state information. For example, this may contain the hidden states
          from an RNN policy.
      step_type (StepType): a :class:`~StepType` enum value. Can be one of
          :attribute:`~StepType.FIRST`, :attribute:`~StepType.MID`,
          :attribute:`~StepType.TERMINAL`, or :attribute:`~StepType.TIMEOUT`.
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docstr: bool: Whether this step records a timeout condition.
docstr_tok: ["bool",":","Whether","this","step","records","a","timeout","condition","."]
code: def timeout(self):
      """bool: Whether this step records a timeout condition."""
      return self.step_type is StepType.TIMEOUT
  
imports: from dataclasses import dataclass
  import enum
  from typing import Dict, List
  import warnings
  import numpy
  from garage.np import concat_tensor_dict_list, pad_batch_array, slice_nested_dict, stack_tensor_dict_list
label_desc: Use image node_id 4 for calling the TimeStep obj's underlying member method code with example usage: obj.timeout() and returns: unknown