class_docstr: Policy which parametrizes independent distributions per action, conditioned on the output of a
  central neural network processing the input state, supporting both a stochastic and value-based
  policy interface (specification key: `parametrized_distributions`).
  
  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
          distributions, via registered tensors with name "[ACTION]-embedding"
          (<span style="color:#00C000"><b>default</b></span>: single output).
      distributions (dict[specification]): Distributions configuration, see
          [distributions](../modules/distributions.html), specified per
          action-type or -name
          (<span style="color:#00C000"><b>default</b></span>: per action-type, Bernoulli
          distribution for binary boolean actions, categorical distribution for discrete integer
          actions, Gaussian distribution for unbounded continuous actions, Beta distribution for
          bounded continuous actions).
      temperature (parameter | dict[parameter], float >= 0.0): Sampling temperature, global or
          per action (<span style="color:#00C000"><b>default</b></span>: 1.0).
      use_beta_distribution (bool): Whether to use the Beta distribution for bounded continuous
          actions by default.
          (<span style="color:#00C000"><b>default</b></span>: false).
      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 distribution in self.distributions.values():
          for variable in distribution.variables:
              assert variable.name not in trackables
              trackables[variable.name] = variable
      return trackables
  
file_path: tensorforce/tensorforce/core/policies/parametrized_distributions.py
imports: import tensorflow
  from tensorforce.core import distribution_modules, ModuleDict, TensorDict, TensorsSpec, tf_function, tf_util
  from tensorforce.core.policies import ParametrizedPolicy, StochasticPolicy, ValuePolicy
label_desc: Use image node_id 5 for calling the ParametrizedDistributions obj's underlying member method code with example usage: obj.get_savedmodel_trackables() and returns: trackables