class_docstr: A Qtopt Agent.
  
  Implements the Qt-Opt algorithm from
  
  "QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic "
    "Manipulation"
  
   Dmitry Kalashnikov et al., 2018
   https://arxiv.org/abs/1806.10293
docstr: Computes auxiliary losses, updating losses_dict in place.
docstr_tok: ["Computes","auxiliary","losses",",","updating","losses_dict","in","place","."]
code: def _add_auxiliary_losses(self, transition, weights, losses_dict):
      """Computes auxiliary losses, updating losses_dict in place."""
      total_auxiliary_loss = 0
      if self._auxiliary_loss_fns is not None:
          for auxiliary_loss_fn in self._auxiliary_loss_fns:
              auxiliary_loss, auxiliary_reg_loss = auxiliary_loss_fn(
                  network=self._q_network, transition=transition
              )
              agg_auxiliary_loss = common.aggregate_losses(
                  per_example_loss=auxiliary_loss,
                  sample_weight=weights,
                  regularization_loss=auxiliary_reg_loss,
              )
              total_auxiliary_loss += agg_auxiliary_loss.total_loss
              losses_dict.update(
                  {
                      "auxiliary_loss_{}".format(
                          auxiliary_loss_fn.__name__
                      ): agg_auxiliary_loss.weighted,
                      "auxiliary_reg_loss_{}".format(
                          auxiliary_loss_fn.__name__
                      ): agg_auxiliary_loss.regularization,
                  }
              )
      return total_auxiliary_loss
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import typing
  import gin
  import tensorflow
  from tf_agents.agents import data_converter
  from tf_agents.agents import tf_agent
  from tf_agents.networks import utils
  from tf_agents.policies import epsilon_greedy_policy
  from tf_agents.policies import qtopt_cem_policy
  from tf_agents.specs import tensor_spec
  from tf_agents.typing import types
  from tf_agents.utils import common
  from tf_agents.utils import eager_utils
  from tf_agents.utils import nest_utils
label_desc: Use image node_id 13 for calling the QtOptAgent obj's underlying member method code with example usage: obj._add_auxiliary_losses(transition, weights, losses_dict) and returns: total_auxiliary_loss