class_docstr: Policy which parametrizes a state-value function, conditioned on the output of a neural network
  processing the input state (specification key: `parametrized_state_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 state_value(self, *, states, horizons, internals, auxiliaries):
      deterministic = tf_util.constant(value=True, dtype="bool")
      embedding, _ = self.network.apply(
          x=states,
          horizons=horizons,
          internals=internals,
          deterministic=deterministic,
          independent=True,
      )
  
      return self.value.apply(x=embedding)
  
imports: from tensorforce.core import layer_modules, tf_function, tf_util
  from tensorforce.core.policies import ParametrizedPolicy, StateValue
label_desc: Use image node_id 3 for calling the ParametrizedStateValue obj's underlying member method code with example usage: obj.state_value() and returns: self