class_docstr: Policy which parametrizes an action-value function, conditioned on the output of a neural
  network processing the input state (specification key: `parametrized_action_value`).
  
  Args:
      network ('auto' | specification): Policy network configuration, see
          [networks](../modules/networks.html)
          (<span style="color:#00C000"><b>default</b></span>: 'auto', automatically configured
          network).
      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>.
code: def action_value(
      self, *, states, horizons, internals, auxiliaries, actions
  ):
      inputs = TensorDict()
      if self.states_spec.is_singleton():
          inputs["states"] = states.singleton()
      else:
          inputs["states"] = states
      if self.actions_spec.is_singleton():
          inputs["actions"] = actions.singleton()
      else:
          inputs["actions"] = actions
  
      deterministic = tf_util.constant(value=True, dtype="bool")
      embedding, _ = self.network.apply(
          x=inputs,
          horizons=horizons,
          internals=internals,
          deterministic=deterministic,
          independent=True,
      )
  
      return self.value.apply(x=embedding)
  
file_path: tensorforce/tensorforce/core/policies/parametrized_action_value.py
imports: from tensorforce.core import layer_modules, TensorDict, TensorsSpec, tf_function, tf_util
  from tensorforce.core.policies import ActionValue, ParametrizedPolicy
label_desc: Use image node_id 4 for calling the ParametrizedActionValue obj's underlying member method code with example usage: obj.action_value() and returns: self