class_docstr: Dynamic Modules are Avalanche modules that can be incrementally
  expanded to allow architectural modifications (multi-head
  classifiers, progressive networks, ...).
  
  Compared to pytoch Modules, they provide an additional method,
  `model_adaptation`, which adapts the model given the current experience.
class_docstr_tok: ["Dynamic","Modules","are","Avalanche","modules","that","can","be","incrementally","expanded","to","allow","architectural","modifications","(","multi-head","classifiers",",","progressive","networks",",","...",")",".","Compared","to","pytoch","Modules",",","they","provide","an","additional","method",",","`","model_adaptation","`",",","which","adapts","the","model","given","the","current","experience","."]
docstr: The device to use when expanding (or otherwise adapting)
  the model. Defaults to the current device of the fist
  parameter listed using :meth:`parameters`.
docstr_tok: ["The","device","to","use","when","expanding","(","or","otherwise","adapting",")","the","model",".","Defaults","to","the","current","device","of","the","fist","parameter","listed","using",":","meth",":","`","parameters","`","."]
code: def _adaptation_device(self):
      """
      The device to use when expanding (or otherwise adapting)
      the model. Defaults to the current device of the fist
      parameter listed using :meth:`parameters`.
      """
      return next(self.parameters()).device
  
imports: import torch
  from torch.nn import Module
  from typing import Optional
  from avalanche.benchmarks.utils.flat_data import ConstantSequence
  from avalanche.benchmarks.scenarios import CLExperience
label_desc: Use image node_id 4 for calling the DynamicModule obj's underlying member method code with example usage: obj._adaptation_device() and returns: next