code: def merge_data(self, data1, data2):
      data = copy.deepcopy(data1)
      for k, v in data1.items():
          if type(v) is paddle.Tensor:
              data[k] = paddle.concat(x=[data[k], data2[k]], axis=0)
          elif type(v) is list:
              data[k].extend(data2[k])
      return data
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import copy
  import time
  import typing
  import numpy
  import paddle
  import paddle.nn
  import paddle.distributed
  from paddle.distributed import fleet
  from ppdet.optimizer import ModelEMA, SimpleModelEMA
  from ppdet.core.workspace import create
  from ppdet.utils.checkpoint import load_weight, load_pretrain_weight, save_model
  import ppdet.utils.stats
  from ppdet.utils import profiler
  from ppdet.modeling.ssod.utils import align_weak_strong_shape
  from .trainer import Trainer
  from ppdet.utils.logger import setup_logger
  from paddle.static import InputSpec
  from ppdet.engine.export_utils import _dump_infer_config, _prune_input_spec
label_desc: Use image node_id 4 for calling the Trainer_ARSL obj's underlying member method code with example usage: obj.merge_data(data1, data2) and returns: data