code: def index_concatenate(x, axis=0):
      if len(x) == 0:
          return None
  
      if is_array_of_what(x, np.ndarray):
          return np.concatenate(x, axis)
      elif is_array_of_what(x, dict):
          dict_res = {}
          for k in x[0].keys():
              dict_res[k] = index_concatenate([item[k] for item in x])
          return [dict_res]
      else:
          res = []
          for i, sample in enumerate(x[0]):
              if isinstance(sample, (list, tuple, np.ndarray)):
                  res.append(index_concatenate([item[i] for item in x]))
              elif isinstance(sample, dict):
                  dict_res = {}
                  for k in sample.keys():
                      dict_res[k] = index_concatenate(
                          [item[i][k] for item in x]
                      )
                  res.append(dict_res)
              else:
                  invalidInputError(
                      False,
                      "data should be an ndarray, a dict of ndarrays,"
                      " a tuple of ndarrays"
                      " or a list of ndarrays, please check your input",
                  )
          return res
  
imports: import time
  import numbers
  import socket
  import collections
  import numpy
  from contextlib import closing, contextmanager
  import torch
  from bigdl.dllib.utils.log4Error import *
label_desc: Use image node_id 9 for calling a global function with example usage: index_concatenate(x, axis) and returns: None, np, list, res