code: def get_param_dict(self, old_param=None, update_all=True):
      if old_param is None:
          result = {}
      else:
          result = old_param
      for i in range(self.layer_nums):
          key = "layer_%d" % i
          new_param = self.layers[i].get_param_dict()
          if key in result:
              new_param = self.merge_param(
                  result[key], new_param, update_all
              )
              result[key] = new_param
          else:
              result[key] = new_param
      if self.residual:
          for i, fc in enumerate(self.fcs):
              key = f"layer_{i}_fc_{fc.weight.size(0)}_{fc.weight.size(1)}"
              result[key] = self.fcs[i]
      if self.batch_normal:
          for i, bn in enumerate(self.bns):
              key = f"layer_{i}_fc_{bn.weight.size(0)}"
              result[key] = self.bns[i]
      return result
  
imports: import torch
  import typing
  import torch.nn
  import torch.nn.functional
  from .None import register_nas_space
  from .base import BaseSpace, map_nn
  from ...model import BaseAutoModel
  from .operation import act_map
  from ..utils import count_parameters, measure_latency
  from ..backend import *
  from operator import *
  from .operation import *
label_desc: Use image node_id 7 for calling the GraphNet obj's underlying member method code with example usage: obj.get_param_dict(old_param, update_all) and returns: result