text: import dask.dataframe as dd
  from dask.dataframe.io.parquet.arrow import ArrowEngine
  
  import cudf
  
  
  class CudfEngine(ArrowEngine):
      @staticmethod
      def read_metadata(*args, **kwargs):
          meta, stats, parts = ArrowEngine.read_metadata(*args, **kwargs)
  
          # If `strings_to_categorical==True`, convert objects to int32
          strings_to_cats = kwargs.get("strings_to_categorical", False)
          dtypes = {}
          for col in meta.columns:
              if meta[col].dtype == "O":
                  dtypes[col] = "int32" if strings_to_cats else "object"
  
          meta = cudf.DataFrame.from_pandas(meta)
          for col, dtype in dtypes.items():
              meta[col] = meta[col].astype(dtype)
  
          return (meta, stats, parts)
  
      @staticmethod
      def read_partition(
          fs, piece, columns, index, categories=(), partitions=(), **kwargs
      ):
          if columns is not None:
              columns = [c for c in columns]
          if isinstance(index, list):
              columns += index
  
          strings_to_cats = kwargs.get("strings_to_categorical", False)
          if cudf.utils.ioutils._is_local_filesystem(fs):
              df = cudf.read_parquet(
                  piece.path,
                  engine="cudf",
                  columns=columns,
                  row_group=piece.row_group,
                  strings_to_categorical=strings_to_cats,
                  **kwargs.get("read", {}),
              )
          else:
              with fs.open(piece.path, mode="rb") as f:
                  df = cudf.read_parquet(
                      f,
                      engine="cudf",
                      columns=columns,
                      row_group=piece.row_group,
                      strings_to_categorical=strings_to_cats,
                      **kwargs.get("read", {}),
                  )
  
          if index is not None and index[0] in df.columns:
              df = df.set_index(index[0])
  
          return df
  
  
  def read_parquet(path, **kwargs):
      """ Read parquet files into a Dask DataFrame
  
      Calls ``dask.dataframe.read_parquet`` to cordinate the execution of
      ``cudf.read_parquet``, and ultimately read multiple partitions into a
      single Dask dataframe. The Dask version must supply an ``ArrowEngine``
      class to support full functionality.
      See ``cudf.read_parquet`` and Dask documentation for further details.
  
      Examples
      --------
      >>> import dask_cudf
      >>> df = dask_cudf.read_parquet("/path/to/dataset/")  # doctest: +SKIP
  
      See Also
      --------
      cudf.read_parquet
      """
  
      columns = kwargs.pop("columns", None)
      if isinstance(columns, str):
          columns = [columns]
      return dd.read_parquet(path, columns=columns, engine=CudfEngine, **kwargs)
  