code: def _dataframe_to_xshards(
      data,
      feature_cols,
      label_cols=None,
      accept_str_col=False,
      shard_size=None,
  ):
      schema = data.schema
      numpy_rdd = data.rdd.map(
          lambda row: convert_row_to_numpy(
              row, schema, feature_cols, label_cols, accept_str_col
          )
      )
      shard_rdd = numpy_rdd.mapPartitions(
          lambda x: arrays2dict(x, feature_cols, label_cols, shard_size)
      )
      return SparkXShards.lazy(shard_rdd, class_name="builtins.dict")
  
imports: from contextlib import closing
  import socket
  import sys
  import tempfile
  import shutil
  from bigdl.dllib.utils import log4Error
  from bigdl.dllib.utils.common import callBigDlFunc
  from bigdl.dllib.utils.file_utils import get_file_list, is_local_path
  from bigdl.orca.data import SparkXShards
  from bigdl.orca.data.utils import get_size
  from bigdl.orca.data.file import put_local_dir_tree_to_remote, put_local_file_to_remote, get_remote_file_to_local, get_remote_dir_to_local
  from bigdl.dllib.utils.utils import convert_row_to_numpy
  from functools import partial
  import numpy
  from bigdl.orca.common import SafePickle
  import os
  import subprocess
  from bigdl.dllib.utils.log4Error import *
label_desc: Use image node_id 17 for calling a global function with example usage: _dataframe_to_xshards(data, feature_cols, label_cols, accept_str_col, shard_size) and returns: SparkXShards