class_docstr: Base class for memories.
  
  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>.
      values_spec (specification): <span style="color:#0000C0"><b>internal use</b></span>.
      min_capacity (int >= 0): <span style="color:#0000C0"><b>internal use</b></span>.
code: def input_signature(self, *, function):
      if function == "enqueue":
          return self.values_spec.signature(batched=True)
  
      elif function == "predecessors":
          return SignatureDict(
              indices=TensorSpec(type="int", shape=()).signature(
                  batched=True
              ),
              horizon=TensorSpec(type="int", shape=()).signature(
                  batched=False
              ),
          )
  
      elif function == "reset":
          return SignatureDict()
  
      elif function == "retrieve":
          return SignatureDict(
              indices=TensorSpec(type="int", shape=()).signature(
                  batched=True
              )
          )
  
      elif function == "retrieve_episodes":
          return SignatureDict(
              n=TensorSpec(type="int", shape=()).signature(
                  batched=False
              )
          )
  
      elif function == "retrieve_timesteps":
          return SignatureDict(
              n=TensorSpec(type="int", shape=()).signature(
                  batched=False
              ),
              past_horizon=TensorSpec(type="int", shape=()).signature(
                  batched=False
              ),
              future_horizon=TensorSpec(type="int", shape=()).signature(
                  batched=False
              ),
          )
  
      elif function == "successors":
          return SignatureDict(
              indices=TensorSpec(type="int", shape=()).signature(
                  batched=True
              ),
              horizon=TensorSpec(type="int", shape=()).signature(
                  batched=False
              ),
          )
  
      else:
          return super().input_signature(function=function)
  
file_path: tensorforce/tensorforce/core/memories/memory.py
imports: from tensorforce.core import Module, SignatureDict, TensorSpec, tf_function
label_desc: Use image node_id 2 for calling the Memory obj's underlying member method code with example usage: obj.input_signature() and returns: self, SignatureDict, SignatureDict, SignatureDict, SignatureDict, SignatureDict, SignatureDict, super