class_docstr: Arguments to call train() or resume().
  
  Args:
      n_epochs (int): Number of epochs.
      batch_size (int): Number of environment steps in one batch.
      plot (bool): Visualize an episode of the policy after after each epoch.
      store_episodes (bool): Save episodes in snapshot.
      pause_for_plot (bool): Pause for plot.
      start_epoch (int): The starting epoch. Used for resume().
code: def __init__(
      self,
      n_epochs,
      batch_size,
      plot,
      store_episodes,
      pause_for_plot,
      start_epoch,
  ):
      self.n_epochs = n_epochs
      self.batch_size = batch_size
      self.plot = plot
      self.store_episodes = store_episodes
      self.pause_for_plot = pause_for_plot
      self.start_epoch = start_epoch
  
imports: import copy
  import os
  import time
  import cloudpickle
  from dowel import logger, tabular
  from garage.experiment.deterministic import get_seed, set_seed
  from garage.experiment.experiment import dump_json
  from garage.experiment.snapshotter import Snapshotter
label_desc: Use image node_id 1 to create a new TrainArgs object  with example: obj = TrainArgs(n_epochs, batch_size, plot, store_episodes, pause_for_plot, start_epoch)