code: def _chunk_numpy_array(
      arr, fmt_meta, chunk_sizes, path_fmt, vector_rows=False
  ):
      paths = []
      offset = 0
  
      for j, n in enumerate(chunk_sizes):
          path = os.path.abspath(path_fmt % j)
          arr_chunk = arr[offset : offset + n]
          shape = arr_chunk.shape
          logging.info("Chunking %d-%d" % (offset, offset + n))
          # If requested we write multi-column arrays as single-column vector Parquet files
          array_parser = array_readwriter.get_array_parser(**fmt_meta)
          if (
              isinstance(array_parser, ParquetArrayParser)
              and len(shape) > 1
              and shape[1] > 1
          ):
              array_parser.write(
                  path, arr_chunk, vector_rows=vector_rows
              )
          else:
              array_parser.write(path, arr_chunk)
          offset += n
          paths.append(path)
  
      return paths
  
file_path: dgl/tests/tools/pytest_utils.py
imports: import json
  import logging
  import os
  import dgl
  import numpy
  import torch
  from distpartitioning import array_readwriter
  from distpartitioning.array_readwriter.parquet import ParquetArrayParser
  from files import setdir
label_desc: Use image node_id 1 for calling a global function with example usage: _chunk_numpy_array(arr, fmt_meta, chunk_sizes, path_fmt, vector_rows) and returns: paths