code: def read_as_dataloader(
      path, config=None, transforms=None, batch_size=1, *args, **kwargs
  ):
      path, _ = pa_fs(path)
      import tensorflow as tf
      import torch
  
      schema_path = os.path.join(path, "_orca_metadata")
      j_str = open_text(schema_path)[0]
      schema = decode_schema(j_str)
  
      row_group = []
  
      for root, dirs, files in os.walk(path):
          for name in dirs:
              if name.startswith("chunk="):
                  chunk_path = os.path.join(path, name)
                  row_group.append(chunk_path)
  
      class ParquetIterableDataset(torch.utils.data.IterableDataset):
          def __init__(
              self,
              row_group,
              schema,
              num_shards=None,
              rank=None,
              transforms=None,
          ):
              super().__init__()
              self.iterator = ParquetIterable(
                  row_group, schema, num_shards, rank, transforms
              )
              self.cur = self.iterator.cur
              self.cur_tail = self.iterator.cur_tail
  
          def __iter__(self):
              return self.iterator.__iter__()
  
          def __next__(self):
              self.iterator.__next__()
  
      def worker_init_fn(w_id):
          worker_info = torch.utils.data.get_worker_info()
          dataset = worker_info.dataset
          iter_start = dataset.cur
          iter_end = dataset.cur_tail
          per_worker = int(
              math.ceil(
                  iter_end - iter_start / float(worker_info.num_workers)
              )
          )
          w_id = worker_info.id
          dataset.cur = iter_start + w_id * per_worker
          dataset.cur_tail = min(dataset.cur + per_worker, iter_end)
  
      dataset = ParquetIterableDataset(
          row_group=row_group,
          schema=schema,
          num_shards=config.get("num_shards"),
          rank=config.get("rank"),
          transforms=transforms,
      )
  
      return torch.utils.data.DataLoader(
          dataset,
          num_workers=config.get("num_workers", 0),
          batch_size=batch_size,
          worker_init_fn=worker_init_fn,
      )
  
imports: from pyspark import SparkContext
  from pyspark.sql import SparkSession
  from zoo import init_nncontext
  from zoo.orca.data import SparkXShards
  from zoo.orca.data.file import open_text, write_text
  from zoo.orca.data.image.utils import chunks, dict_to_row, row_to_dict, encode_schema, decode_schema, SchemaField, FeatureType, DType, ndarray_dtype_to_dtype, decode_feature_type_ndarray, pa_fs
  from zoo.orca.data.image.voc_dataset import VOCDatasets
  from bigdl.util.common import get_node_and_core_number
  import os
  import numpy
  import random
  import pyarrow.parquet
  import io
  import math
label_desc: Use image node_id 11 for calling a global function with example usage: read_as_dataloader(path, config, transforms, batch_size) and returns: torch, self