class_docstr: Trust Region Policy Optimization (https://arxiv.org/abs/1502.05477)
  
  :param policy: (ActorCriticPolicy or str) The policy model to use (MlpPolicy, CnnPolicy, CnnLstmPolicy, ...)
  :param env: (Gym environment or str) The environment to learn from (if registered in Gym, can be str)
  :param gamma: (float) the discount value
  :param timesteps_per_batch: (int) the number of timesteps to run per batch (horizon)
  :param max_kl: (float) the Kullback-Leibler loss threshold
  :param cg_iters: (int) the number of iterations for the conjugate gradient calculation
  :param lam: (float) GAE factor
  :param entcoeff: (float) the weight for the entropy loss
  :param cg_damping: (float) the compute gradient dampening factor
  :param vf_stepsize: (float) the value function stepsize
  :param vf_iters: (int) the value function's number iterations for learning
  :param verbose: (int) the verbosity level: 0 none, 1 training information, 2 tensorflow debug
  :param tensorboard_log: (str) the log location for tensorboard (if None, no logging)
  :param _init_setup_model: (bool) Whether or not to build the network at the creation of the instance
  :param policy_kwargs: (dict) additional arguments to be passed to the policy on creation
  :param full_tensorboard_log: (bool) enable additional logging when using tensorboard
      WARNING: this logging can take a lot of space quickly
  :param seed: (int) Seed for the pseudo-random generators (python, numpy, tensorflow).
      If None (default), use random seed. Note that if you want completely deterministic
      results, you must set `n_cpu_tf_sess` to 1.
  :param n_cpu_tf_sess: (int) The number of threads for TensorFlow operations
      If None, the number of cpu of the current machine will be used.
docstr: Initialize dataloader.
docstr_tok: ["Initialize","dataloader","."]
code: def _initialize_dataloader(self):
      """Initialize dataloader."""
      batchsize = self.timesteps_per_batch // self.d_step
      self.expert_dataset.init_dataloader(batchsize)
  
imports: import time
  from contextlib import contextmanager
  from collections import deque
  import gym
  from mpi4py import MPI
  import tensorflow
  import numpy
  import stable_baselines.common.tf_util
  from stable_baselines.common.tf_util import total_episode_reward_logger
  from stable_baselines.common import explained_variance, zipsame, dataset, fmt_row, colorize, ActorCriticRLModel, SetVerbosity, TensorboardWriter
  from stable_baselines import logger
  from stable_baselines.common.mpi_adam import MpiAdam
  from stable_baselines.common.cg import conjugate_gradient
  from stable_baselines.common.policies import ActorCriticPolicy
  from stable_baselines.common.misc_util import flatten_lists
  from stable_baselines.common.runners import traj_segment_generator
  from stable_baselines.trpo_mpi.utils import add_vtarg_and_adv
label_desc: Use image node_id 4 for calling the TRPO obj's underlying member method code with example usage: obj._initialize_dataloader() without return types