class_docstr: The box wrapping a space, can be passed to later procedure or trainer
  
  Parameters
  ----------
  space_model : BaseSpace
      The space which should be wrapped
  device : str or torch.device
      The device to place the model
docstr: receive no hp, just copy self and reset the learnable parameters.
docstr_tok: ["receive","no","hp",",","just","copy","self","and","reset","the","learnable","parameters","."]
code: def from_hyper_parameter(self, hp):
      """
      receive no hp, just copy self and reset the learnable parameters.
      """
  
      ret_self = deepcopy(self)
      ret_self._model.instantiate()
      if ret_self.selection:
          apply_fixed_architecture(
              ret_self._model, ret_self.selection, verbose=False
          )
      return ret_self
  
imports: from abc import abstractmethod
  import torch.nn
  from nni.nas.pytorch import mutables
  from nni.nas.pytorch.fixed import FixedArchitecture
  import json
  from copy import deepcopy
  import torch
  from ...model import BaseAutoModel
  from ....utils import get_logger
  from ..utils import get_hardware_aware_metric
label_desc: Use image node_id 5 for calling the BoxModel obj's underlying member method code with example usage: obj.from_hyper_parameter(hp) and returns: ret_self