code: def _process_model(self, model):
      to_agg = None
      if isinstance(model, np.ndarray) or isinstance(model, t.Tensor):
          to_agg = self._convert_type(model, self.float_p)
          return [to_agg]
  
      if isinstance(model, t.nn.Module):
          parameters = list(model.parameters())
          if self.require_grad:
              agg_list = [
                  self._convert_type(
                      p.cpu().detach().numpy(), self.float_p
                  )
                  for p in parameters
                  if p.requires_grad
              ]
          else:
              agg_list = [
                  self._convert_type(
                      p.cpu().detach().numpy(), self.float_p
                  )
                  for p in parameters
              ]
  
      elif isinstance(model, list):
          to_agg = []
          for p in model:
              to_agg.append(self._convert_type(p, self.float_p))
          agg_list = to_agg
  
      else:
          return None
  
      return agg_list
  
imports: import logging
  from typing import Optional
  import numpy
  import torch
  from fate.arch import Context
  from fate.arch.protocol.secure_aggregation._secure_aggregation import SecureAggregatorClient
  from fate.arch.protocol.secure_aggregation._secure_aggregation import SecureAggregatorServer
label_desc: Use image node_id 3 for calling the BaseAggregatorClient obj's underlying member method code with example usage: obj._process_model(model) and returns: agg_list, list, None