code: def _handle_xshards(self, dataset, steps, local_batch_size, shuffle):
      import tensorflow as tf
  
      data, label = partition_get_data_label(
          dataset, allow_tuple=True, allow_list=False
      )
  
      def dataset_fn(input_context):
          dataset = tf.data.Dataset.from_tensor_slices((data, label))
          options = tf.data.Options()
          options.experimental_distribute.auto_shard_policy = (
              tf.data.experimental.AutoShardPolicy.OFF
          )
          dataset = dataset.with_options(options)
          dataset = dataset.repeat()
          dataset = dataset.take(steps * local_batch_size)
          if shuffle:
              dataset = dataset.shuffle(
                  local_batch_size * min(steps, 10)
              )
          dataset = dataset.batch(local_batch_size)
          return dataset
  
      from tensorflow.python.distribute import (
          distribution_strategy_context as ds_context,
      )
  
      strategy = ds_context.get_strategy()
      dataset = strategy.experimental_distribute_datasets_from_function(
          dataset_fn
      )
      return dataset
  
imports: import json
  import os
  import tempfile
  import shutil
  import copy
  import tensorflow
  from bigdl.orca.data.utils import partition_get_data_label
  from bigdl.orca.data.file import exists
  from bigdl.orca.learn.utils import save_pkl, duplicate_stdout_stderr_to_file, get_rank, process_tensorboard_in_callbacks, save_model, load_model, replace_specific_object_from_callbacks
  from bigdl.orca.learn.log_monitor import LogMonitor
  from bigdl.orca.learn.tf2.callbacks import ModelCheckpoint
  from bigdl.dllib.utils.log4Error import *
label_desc: Use image node_id 1 for calling the TFDistributedDatasetHandler obj's underlying member method code with example usage: obj._handle_xshards(dataset, steps, local_batch_size, shuffle) and returns: dataset, dataset