code: def read_partition(
      cls,
      fs,
      pieces,
      columns,
      index,
      categories=(),
      partitions=(),
      filters=None,
      partitioning=None,
      schema=None,
      open_file_options=None,
      **kwargs,
  ):
      if columns is not None:
          columns = [c for c in columns]
      if isinstance(index, list):
          columns += index
  
      dataset_kwargs = kwargs.get("dataset", {})
      partitioning = partitioning or dataset_kwargs.get(
          "partitioning", None
      )
      if isinstance(partitioning, dict):
          partitioning = pa_ds.partitioning(**partitioning)
  
      # Check if we are actually selecting any columns
      read_columns = columns
      if schema and columns:
          ignored = set(schema.names) - set(columns)
          if not ignored:
              read_columns = None
  
      if not isinstance(pieces, list):
          pieces = [pieces]
  
      # Extract supported kwargs from `kwargs`
      read_kwargs = kwargs.get("read", {})
      read_kwargs.update(open_file_options or {})
      check_file_size = read_kwargs.pop("check_file_size", None)
  
      # Wrap reading logic in a `try` block so that we can
      # inform the user that the `read_parquet` partition
      # size is too large for the available memory
      try:
          # Assume multi-piece read
          paths = []
          rgs = []
          last_partition_keys = None
          dfs = []
  
          for i, piece in enumerate(pieces):
              (path, row_group, partition_keys) = piece
              row_group = None if row_group == [None] else row_group
  
              # File-size check to help "protect" users from change
              # to up-stream `split_row_groups` default. We only
              # check the file size if this partition corresponds
              # to a full file, and `check_file_size` is defined
              if (
                  check_file_size
                  and len(pieces) == 1
                  and row_group is None
              ):
                  file_size = fs.size(path)
                  if file_size > check_file_size:
                      warnings.warn(
                          f"A large parquet file ({file_size}B) is being "
                          f"used to create a DataFrame partition in "
                          f"read_parquet. This may cause out of memory "
                          f"exceptions in operations downstream. See the "
                          f"notes on split_row_groups in the read_parquet "
                          f"documentation. Setting split_row_groups "
                          f"explicitly will silence this warning."
                      )
  
              if i > 0 and partition_keys != last_partition_keys:
                  dfs.append(
                      cls._read_paths(
                          paths,
                          fs,
                          columns=read_columns,
                          row_groups=rgs if rgs else None,
                          filters=filters,
                          partitions=partitions,
                          partitioning=partitioning,
                          partition_keys=last_partition_keys,
                          dataset_kwargs=dataset_kwargs,
                          **read_kwargs,
                      )
                  )
                  paths = []
                  rgs = []
                  last_partition_keys = None
              paths.append(path)
              rgs.append(
                  [row_group]
                  if not isinstance(row_group, list)
                  and row_group is not None
                  else row_group
              )
              last_partition_keys = partition_keys
  
          dfs.append(
              cls._read_paths(
                  paths,
                  fs,
                  columns=read_columns,
                  row_groups=rgs if rgs else None,
                  filters=filters,
                  partitions=partitions,
                  partitioning=partitioning,
                  partition_keys=last_partition_keys,
                  dataset_kwargs=dataset_kwargs,
                  **read_kwargs,
              )
          )
          df = cudf.concat(dfs) if len(dfs) > 1 else dfs[0]
  
          # Re-set "object" dtypes align with pa schema
          set_object_dtypes_from_pa_schema(df, schema)
  
          if index and (index[0] in df.columns):
              df = df.set_index(index[0])
          elif index is False and df.index.names != (None,):
              # If index=False, we shouldn't have a named index
              df.reset_index(inplace=True)
  
      except MemoryError as err:
          raise MemoryError(
              "Parquet data was larger than the available GPU memory!\n\n"
              "See the notes on split_row_groups in the read_parquet "
              "documentation.\n\n"
              "Original Error: " + str(err)
          )
          raise err
  
      return df
  
imports: import itertools
  import warnings
  from contextlib import ExitStack
  from functools import partial
  from io import BufferedWriter, BytesIO, IOBase
  import numpy
  from pyarrow import dataset, parquet
  from dask import dataframe
  from dask.dataframe.io.parquet.arrow import ArrowDatasetEngine
  import cudf
  from cudf.core.column import as_column, build_categorical_column
  from cudf.io import write_to_dataset
  from cudf.io.parquet import _apply_post_filters, _default_open_file_options, _normalize_filters
  from cudf.utils.dtypes import cudf_dtype_from_pa_type
  from cudf.utils.ioutils import _ROW_GROUP_SIZE_BYTES_DEFAULT, _is_local_filesystem, _open_remote_files
label_desc: Use image node_id 4 for calling the CudfEngine obj's underlying member method code with example usage: obj.read_partition(cls, fs, pieces, columns, index, categories, partitions, filters, partitioning, schema, open_file_options) and returns: df