class_docstr: Special preprocessor network following a sequential layer-stack architecture, which can be
  specified as either a single or a list of layer specifications.
  
  Args:
      layers (iter[specification] | iter[iter[specification]]): Layers configuration, see the
          [layers documentation](../modules/layers.html)
          (<span style="color:#C00000"><b>required</b></span>).
      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).
      is_preprocessing_layer_valid (bool): <span style="color:#0000C0"><b>internal use</b></span>.
      name (string): <span style="color:#0000C0"><b>internal use</b></span>.
      input_spec (specification): <span style="color:#0000C0"><b>internal use</b></span>.
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code: def input_signature(self, *, function):
      if function == "apply":
          return SignatureDict(
              x=self.inputs_spec.signature(batched=True),
              deterministic=TensorSpec(type="bool", shape=()).signature(
                  batched=False
              ),
          )
  
      elif function == "reset":
          return SignatureDict()
  
      else:
          return super().input_signature(function=function)
  
file_path: tensorforce/tensorforce/core/networks/preprocessor.py
imports: import tensorflow
  from tensorforce import TensorforceError
  from tensorforce.core import SignatureDict, TensorDict, TensorSpec, TensorsSpec, tf_function, tf_util
  from tensorforce.core.layers import MultiInputLayer, NondeterministicLayer, PreprocessingLayer, Register, StatefulLayer, TemporalLayer
  from tensorforce.core.networks import LayeredNetwork
label_desc: Use image node_id 7 for calling the Preprocessor obj's underlying member method code with example usage: obj.input_signature() and returns: SignatureDict, SignatureDict, super