class_docstr: Policy which parametrizes independent action-/advantage-/state-value functions per action and
  optionally a state-value function, conditioned on the output of a central neural network
  processing the input state (specification key: `parametrized_value_policy`).
  
  Args:
      network ('auto' | specification): Policy network configuration, see
          [networks](../modules/networks.html)
          (<span style="color:#00C000"><b>default</b></span>: 'auto', automatically configured
          network).
      single_output (bool): Whether the network returns a single embedding tensor or, in the case
          of multiple action components, specifies additional outputs for some/all action/state
          value functions, via registered tensors with name "[ACTION]-embedding" or
          "state-embedding"/"[ACTION]-state-embedding" depending on the state_value_mode argument
          (<span style="color:#00C000"><b>default</b></span>: single output).
      state_value_mode ('implicit' | 'separate' | 'separate-per-action'): Whether to compute the
          state value implicitly as maximum action value (like DQN), or as either a single
          separate state-value function or a function per action (like DuelingDQN)
          (<span style="color:#00C000"><b>default</b></span>: single separate state-value
          function).
      device (string): Device name
          (<span style="color:#00C000"><b>default</b></span>: inherit value of parent module).
      l2_regularization (float >= 0.0): Scalar controlling L2 regularization
          (<span style="color:#00C000"><b>default</b></span>: inherit value of parent module).
      name (string): <span style="color:#0000C0"><b>internal use</b></span>.
      states_spec (specification): <span style="color:#0000C0"><b>internal use</b></span>.
      auxiliaries_spec (specification): <span style="color:#0000C0"><b>internal use</b></span>.
      internals_spec (specification): <span style="color:#0000C0"><b>internal use</b></span>.
      actions_spec (specification): <span style="color:#0000C0"><b>internal use</b></span>.
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code: def get_savedmodel_trackables(self):
      trackables = super().get_savedmodel_trackables()
      for a_value in self.a_values.values():
          for variable in a_value.variables:
              assert variable.name not in trackables
              trackables[variable.name] = variable
      if self.state_value_mode == "separate":
          for variable in self.s_value.variables:
              assert variable.name not in trackables
              trackables[variable.name] = variable
      elif self.state_value_mode == "separate-per-action":
          for s_value in self.s_values.values():
              for variable in s_value.variables:
                  assert variable.name not in trackables
                  trackables[variable.name] = variable
      return trackables
  
file_path: tensorforce/tensorforce/core/policies/parametrized_value_policy.py
imports: import tensorflow
  from tensorforce import TensorforceError
  from tensorforce.core import layer_modules, ModuleDict, TensorDict, TensorSpec, TensorsSpec, tf_function, tf_util
  from tensorforce.core.policies import ParametrizedPolicy, ValuePolicy
label_desc: Use image node_id 4 for calling the ParametrizedValuePolicy obj's underlying member method code with example usage: obj.get_savedmodel_trackables() and returns: trackables