class_docstr: Base class for decision policies and "degenerate" value functions.
  
  Args:
      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>.
      actions_spec (specification): <span style="color:#0000C0"><b>internal use</b></span>.
code: def input_signature(self, *, function):
      if function == "next_internals":
          return SignatureDict(
              states=self.states_spec.signature(batched=True),
              horizons=TensorSpec(type="int", shape=(2,)).signature(
                  batched=True
              ),
              internals=self.internals_spec.signature(batched=True),
              actions=self.actions_spec.signature(batched=True),
              deterministic=TensorSpec(type="bool", shape=()).signature(
                  batched=False
              ),
          )
  
      elif function == "past_horizon":
          return SignatureDict()
  
      else:
          return super().input_signature(function=function)
  
file_path: tensorforce/tensorforce/core/policies/base_policy.py
imports: from tensorforce.core import Module, SignatureDict, TensorDict, TensorSpec, TensorsSpec, tf_function
label_desc: Use image node_id 5 for calling the BasePolicy obj's underlying member method code with example usage: obj.input_signature() and returns: SignatureDict, SignatureDict, super