code: def _tile(cls, op: "DataFrameReadCSV"):
      if op.compression:
          return cls._tile_compressed(op)
  
      df = op.outputs[0]
      chunk_bytes = df.extra_params.chunk_bytes
      chunk_bytes = int(parse_readable_size(chunk_bytes)[0])
  
      dtypes = df.dtypes
      if (
          op.use_arrow_dtype is None
          and not op.gpu
          and options.dataframe.use_arrow_dtype
      ):  # pragma: no cover
          # check if use_arrow_dtype set on the server side
          dtypes = to_arrow_dtypes(df.dtypes)
  
      path_prefix = ""
      if isinstance(op.path, (tuple, list)):
          paths = op.path
      elif get_fs(op.path, op.storage_options).isdir(op.path):
          parsed_path = urlparse(op.path)
          if parsed_path.scheme.lower() == "hdfs":
              path_prefix = (
                  f"{parsed_path.scheme}://{parsed_path.netloc}"
              )
              paths = get_fs(op.path, op.storage_options).ls(op.path)
          else:
              paths = glob(
                  op.path.rstrip("/") + "/*",
                  storage_options=op.storage_options,
              )
      else:
          paths = glob(op.path, storage_options=op.storage_options)
  
      out_chunks = []
      index_num = 0
      for path in paths:
          path = path_prefix + path
          total_bytes = file_size(
              path, storage_options=op.storage_options
          )
          offset = 0
          for _ in range(int(np.ceil(total_bytes * 1.0 / chunk_bytes))):
              chunk_op = op.copy().reset_key()
              chunk_op.path = path
              chunk_op.offset = offset
              chunk_op.size = min(chunk_bytes, total_bytes - offset)
              shape = (np.nan, len(dtypes))
              index_value = parse_index(
                  df.index_value.to_pandas(), path, index_num
              )
              new_chunk = chunk_op.new_chunk(
                  None,
                  shape=shape,
                  index=(index_num, 0),
                  index_value=index_value,
                  columns_value=df.columns_value,
                  dtypes=dtypes,
              )
              out_chunks.append(new_chunk)
              index_num += 1
              offset += chunk_bytes
  
      new_op = op.copy()
      nsplits = ((np.nan,) * len(out_chunks), (df.shape[1],))
      df = new_op.new_dataframe(
          None,
          df.shape,
          dtypes=dtypes,
          index_value=df.index_value,
          columns_value=df.columns_value,
          chunks=out_chunks,
          nsplits=nsplits,
      )
      if op.merge_small_files:
          df = merge_small_files(
              df, **(op.merge_small_file_options or dict())
          )
      return [df]
  
imports: from io import BytesIO
  from urllib.parse import urlparse
  import pandas
  import numpy
  from ...None import opcodes
  from ...config import options
  from ...core import OutputType
  from ...lib.filesystem import get_fs, open_file, file_size, glob
  from ...serialization.serializables import StringField, DictField, ListField, Int32Field, Int64Field, BoolField, AnyField
  from ...utils import parse_readable_size, lazy_import, FixedSizeFileObject
  from ..arrays import ArrowStringDtype
  from ..utils import parse_index, build_empty_df, to_arrow_dtypes, contain_arrow_dtype
  from .core import IncrementalIndexDatasource, ColumnPruneSupportedDataSourceMixin, IncrementalIndexDataSourceMixin, merge_small_files
label_desc: Use image node_id 4 for calling the DataFrameReadCSV obj's underlying member method code with example usage: obj._tile(cls, op) and returns: list, cls