code: def aggregate(self, phase):
      input, target = [], None
      # to record the order of tensors with client ID
      for cid, ndarray_map in self.client_data[phase].items():
          for k, v in ndarray_map.items():
              if k == "input":
                  input.append((cid, torch.from_numpy(v)))
              elif k == "target":
                  target = torch.from_numpy(v)
              else:
                  invalidInputError(
                      False,
                      f"Invalid type of tensor map key: {k},"
                      f" should be input/target",
                  )
      # input is a list of tensors
  
      # x = torch.stack(input)
      # x = torch.sum(x, dim=0)
      # x.requires_grad = True
      # pred = self.model(x)
  
      # sort the input tensor list in order to keep the order info of client ID
      def sort_by_key(kv_tuple):
          return kv_tuple[0]
  
      input.sort(key=sort_by_key)
      tensor_list = []
      for cid, input_tensor in input:
          input_tensor.requires_grad = True
          tensor_list.append(input_tensor)
  
      if phase == "train":
          pred = self.model(tensor_list)
          loss = self.loss_fn(pred, target)
          if self.optimizer is not None:
              self.optimizer.zero_grad()
          loss.backward()
          if self.optimizer is not None:
              self.optimizer.step()
  
          for cid, input_tensor in input:
              grad_map = {
                  "grad": input_tensor.grad.numpy(),
                  "loss": loss.detach().numpy(),
              }
              self.server_data["train"][
                  cid
              ] = ndarray_map_to_tensor_map(grad_map)
  
      elif phase == "eval":
          pass
      elif phase == "pred":
          pred = self.model(tensor_list)
          for cid, input_tensor in input:
              pred_map = {"pred": pred.detach().numpy()}
              self.server_data["pred"][cid] = ndarray_map_to_tensor_map(
                  pred_map
              )
      else:
          invalidInputError(
              False,
              f"Invalid phase: {phase}, should be train/eval/pred",
          )
  
imports: import io
  from bigdl.ppml.utils.safepickle import SafePickle
  import logging
  import threading
  from torch import nn
  import torch
  from bigdl.ppml.fl.nn.utils import ndarray_map_to_tensor_map
  from bigdl.dllib.utils.log4Error import invalidInputError, invalidOperationError
  from threading import Condition
  import os
  from bigdl.dllib.utils.encryption_utils import *
label_desc: Use image node_id 6 for calling the Aggregator obj's underlying member method code with example usage: obj.aggregate(phase) and returns: kv_tuple