class_docstr: Base class for optimization objectives.
  
  Args:
      name (string): <span style="color:#0000C0"><b>internal use</b></span>.
      states_spec (specification): <span style="color:#0000C0"><b>internal use</b></span>.
      internals_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>.
      reward_spec (specification): <span style="color:#0000C0"><b>internal use</b></span>.
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code: def output_signature(self, *, function):
      if function == "loss":
          return SignatureDict(
              singleton=TensorSpec(type="float", shape=()).signature(
                  batched=True
              )
          )
  
      elif function == "reference":
          return SignatureDict(
              singleton=self.reference_spec().signature(batched=True)
          )
  
      else:
          return super().output_signature(function=function)
  
file_path: tensorforce/tensorforce/core/objectives/objective.py
imports: from tensorforce.core import Module, SignatureDict, TensorSpec, tf_function
label_desc: Use image node_id 6 for calling the Objective obj's underlying member method code with example usage: obj.output_signature() and returns: SignatureDict, SignatureDict, super