code: def split_id_and_data(d, is_xshards_distributed=False):
      if "y" in d:
          train_data = d["y"]
          if not isinstance(train_data, np.ndarray):
              raise ValueError("the value of y should be an ndarray")
      else:
          raise ValueError("key `y` doesn't exist in x")
      id_arr = None
      if "id" in d:
          id_arr = d["id"]
          if len(id_arr) != train_data.shape[0]:
              raise ValueError(
                  "the length of the id array should be equal to the number of "
                  "rows in the y"
              )
      elif is_xshards_distributed:
          raise ValueError("key `id` doesn't exist in x")
      return id_arr, train_data
  
imports: from abc import ABCMeta, abstractmethod
  from zoo.chronos.model.tcmf import DeepGLO
  from zoo.orca.automl.metrics import Evaluator
  from zoo.orca.automl.model.abstract import BaseModel
  from zoo.orca.data import SparkXShards, XShards
  import pickle
  import numpy
  import pandas
label_desc: Use image node_id 1 for calling a global function with example usage: split_id_and_data(d, is_xshards_distributed) and returns: id_arr,  train_data