code: def combine(data_list):
      item = data_list[0]
      if isinstance(item, dict):
          res = {}
          for k, v in item.items():
              res[k] = np.concatenate(
                  [data[k] for data in data_list], axis=0
              )
      elif isinstance(item, (list, tuple)):
          res = []
          for i in range(len(item)):
              res.append(
                  np.concatenate(
                      [data[i] for data in data_list], axis=0
                  )
              )
          if isinstance(item, tuple):
              res = tuple(res)
      elif isinstance(data_list[0], np.ndarray):
          res = np.concatenate(data_list, axis=0)
      else:
          invalidInputError(
              False,
              "value of x and y should be an ndarray, a dict of ndarrays, a tuple"
              " of ndarrays or a list of ndarrays, please check your input",
          )
      return res
  
imports: from bigdl.dllib.utils.file_utils import get_file_list, callZooFunc
  from bigdl.dllib.utils.utils import convert_row_to_numpy
  from bigdl.dllib.utils.common import *
  from bigdl.dllib.utils.log4Error import *
  import pyspark.sql.functions
  from typing import TYPE_CHECKING, Dict, List, Optional, Type, Union
  from numpy import float32, int32
label_desc: Use image node_id 6 for calling a global function with example usage: combine(data_list) and returns: res