class_docstr: Basically a batch of active trajectories and a list of completed ones.
class_docstr_tok: ["Basically","a","batch","of","active","trajectories","and","a","list","of","completed","ones","."]
code: def __init__(
      self, batch_size=1, trajectories=None, completed_trajectories=None
  ):
      self.batch_size = batch_size
  
      # Stores trajectories that are currently active, i.e. aren't done or reset.
      self._trajectories = trajectories or [
          Trajectory() for _ in range(self.batch_size)
      ]
  
      # Stores trajectories that are completed.
      # NOTE: We don't track the index this came from, as it's not needed, right?
      self._completed_trajectories = completed_trajectories or []
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import pickle
  import re
  import sys
  import time
  from absl import logging
  import cloudpickle
  import numpy
  from tensor2tensor.envs import time_step
  import tensorflow.compat.v1
label_desc: Use image node_id 1 to create a new BatchTrajectory object from inherited base classes: object with example: obj = BatchTrajectory(batch_size, trajectories, completed_trajectories)