class_docstr: Base class for optimizers.
  
  Args:
      name (string): (<span style="color:#0000C0"><b>internal use</b></span>).
      arguments_spec (specification): <span style="color:#0000C0"><b>internal use</b></span>.
code: def output_signature(self, *, function):
      if function == "step":
          return self.variables_spec.fmap(
              function=(lambda spec: spec.signature(batched=False)),
              cls=SignatureDict,
          )
  
      elif function == "update":
          return SignatureDict(
              singleton=TensorSpec(type="bool", shape=()).signature(
                  batched=False
              )
          )
  
      else:
          return super().output_signature(function=function)
  
file_path: tensorforce/tensorforce/core/optimizers/optimizer.py
imports: import tensorflow
  from tensorforce.core import Module, SignatureDict, TensorSpec, TensorsSpec, tf_function, tf_util
label_desc: Use image node_id 4 for calling the Optimizer obj's underlying member method code with example usage: obj.output_signature() and returns: self, SignatureDict, super