code: def read_partition(
      cls,
      fs,
      pieces,
      columns,
      index,
      dtype_backend=None,
      categories=(),
      root_cats=None,
      root_file_scheme=None,
      base_path=None,
      **kwargs,
  ):
      null_index_name = False
      base_path = False if not root_cats else base_path
      if isinstance(index, list):
          if index == [None]:
              # Handling a None-labeled index...
              # The pandas metadata told us to read in an index
              # labeled `None`. If this corresponds to a `RangeIndex`,
              # fastparquet will need use the pandas metadata to
              # construct the index. Otherwise, the index will correspond
              # to a column named "__index_level_0__".  We will need to
              # check the `ParquetFile` object for this column below.
              index = []
              null_index_name = True
          columns += index
  
      # Use global `parquet_file` object.  Need to reattach
      # the desired row_group
      parquet_file = kwargs.pop("parquet_file", None)
  
      # Always convert pieces to list
      if not isinstance(pieces, list):
          pieces = [pieces]
  
      sample = pieces[0]
      if isinstance(sample, tuple):
          if isinstance(sample[0], str):
              # We have paths to read from
              assert parquet_file is None
  
              row_groups = []
              rg_offset = 0
              parquet_file = ParquetFile(
                  [p[0] for p in pieces],
                  open_with=fs.open,
                  root=base_path or False,
                  **kwargs.get("dataset", {}),
              )
              for piece in pieces:
                  _pf = (
                      parquet_file
                      if len(pieces) == 1
                      else ParquetFile(
                          piece[0],
                          open_with=fs.open,
                          root=base_path or False,
                          **kwargs.get("dataset", {}),
                      )
                  )
                  n_local_row_groups = len(_pf.row_groups)
                  local_rg_indices = piece[1] or list(
                      range(n_local_row_groups)
                  )
                  row_groups += [
                      parquet_file.row_groups[rg + rg_offset]
                      for rg in local_rg_indices
                  ]
                  rg_offset += n_local_row_groups
              update_parquet_file = len(row_groups) < len(
                  parquet_file.row_groups
              )
  
          elif parquet_file:
              row_groups = []
              for piece in pieces:
                  # `piece[1]` will contain actual row-group objects,
                  # but they may be pickled
                  rgs = piece[0]
                  if isinstance(rgs, bytes):
                      rgs = pickle.loads(rgs)
                  row_groups += rgs
              update_parquet_file = True
  
          else:
              raise ValueError("Neither path nor ParquetFile detected!")
  
          if update_parquet_file:
              with _FP_FILE_LOCK:
                  for rg in row_groups:
                      for chunk in rg.columns:
                          s = chunk.file_path
                          if s and isinstance(s, bytes):
                              chunk.file_path = s.decode()
  
                  parquet_file.fmd.row_groups = row_groups
                  # NOTE: May lose cats after `_set_attrs` call
                  save_cats = parquet_file.cats
                  parquet_file._set_attrs()
                  parquet_file.cats = save_cats
  
          if null_index_name:
              if "__index_level_0__" in parquet_file.columns:
                  # See "Handling a None-labeled index" comment above
                  index = ["__index_level_0__"]
                  columns += index
  
          # Update hive-partitioning information if necessary
          parquet_file.cats = root_cats or {}
          if root_cats:
              parquet_file.file_scheme = root_file_scheme
  
          parquet_file._dtypes = (
              lambda *args: parquet_file.dtypes
          )  # ugly patch, could be fixed
  
          # Convert ParquetFile to pandas
          return cls.pf_to_pandas(
              parquet_file,
              fs=fs,
              columns=columns,
              categories=categories,
              index=index,
              **kwargs.get("read", {}),
          )
  
      else:
          # `sample` is NOT a tuple
          raise ValueError(f"Expected tuple, got {type(sample)}")
  
imports: from __future__ import annotations
  import copy
  import pickle
  import threading
  import warnings
  from collections import OrderedDict, defaultdict
  from contextlib import ExitStack
  import numpy
  import pandas
  import tlz
  from packaging.version import parse
  from dask.core import flatten
  from dask.dataframe._compat import PANDAS_GE_201
  from dask.base import tokenize
  from dask.dataframe.io.parquet.utils import Engine, _get_aggregation_depth, _infer_split_row_groups, _normalize_index_columns, _parse_pandas_metadata, _process_open_file_options, _row_groups_to_parts, _set_gather_statistics, _set_metadata_task_size, _sort_and_analyze_paths
  from dask.dataframe.io.utils import _is_local_fs, _meta_from_dtypes, _open_input_files
  from dask.dataframe.utils import UNKNOWN_CATEGORIES
  from dask.delayed import Delayed
  from dask.utils import natural_sort_key
label_desc: Use image node_id 10 for calling the FastParquetEngine obj's underlying member method code with example usage: obj.read_partition(cls, fs, pieces, columns, index, dtype_backend, categories, root_cats, root_file_scheme, base_path) and returns: cls