class_docstr: A trajectory of interactions with a RL environment.
  
  Trajectories are created when interacting with an RL environment. They can
  be prolonged and sliced and when completed, allow to re-calculate returns.
class_docstr_tok: ["A","trajectory","of","interactions","with","a","RL","environment",".","Trajectories","are","created","when","interacting","with","an","RL","environment",".","They","can","be","prolonged","and","sliced","and","when","completed",",","allow","to","re-calculate","returns","."]
code: def __init__(self, observation):
      # TODO(lukaszkaiser): add support for saving and loading trajectories,
      # reuse code from base_trainer.dump_trajectories and related functions.
      self._last_observation = observation
      self._timesteps = []
      self._timestep_batch = None
      self._cached_to_np_args = None
  
imports: import collections
  import os
  import gin
  import gym
  import numpy
  from trax import fastmath
  from trax.rl import advantages
  from trax.supervised import training
label_desc: Use image node_id 1 to create a new Trajectory object  with example: obj = Trajectory(observation)