code: def pf_to_pandas(
      cls,
      pf,
      fs=None,
      columns=None,
      categories=None,
      index=None,
      open_file_options=None,
      **kwargs,
  ):
      # This method was mostly copied from the fastparquet
      # `ParquetFile.to_pandas` definition. We maintain our
      # own implmentation in Dask to enable better remote
      # file-handling control
  
      # Handle selected columns
      if columns is not None:
          columns = columns[:]
      else:
          columns = pf.columns + list(pf.cats)
      if index:
          columns += [i for i in index if i not in columns]
  
      # Extract row-groups and pre-allocate df
      rgs = pf.row_groups
      size = sum(rg.num_rows for rg in rgs)
      df, views = pf.pre_allocate(size, columns, categories, index)
      if (
          parse_version(fastparquet.__version__)
          <= parse_version("2023.02.0")
          and PANDAS_GE_201
          and df.columns.empty
      ):
          df.columns = pd.Index([], dtype=object)
      start = 0
  
      # Get a map of file names -> row-groups
      fn_rg_map = defaultdict(list)
      for rg in rgs:
          fn = pf.row_group_filename(rg)
          fn_rg_map[fn].append(rg)
  
      # Define file-opening options
      precache_options, open_file_options = _process_open_file_options(
          open_file_options,
          **(
              {
                  "allow_precache": False,
                  "default_cache": "readahead",
              }
              if _is_local_fs(fs)
              else {
                  "metadata": pf,
                  "columns": list(
                      set(columns).intersection(pf.columns)
                  ),
                  "row_groups": [rgs for rgs in fn_rg_map.values()],
                  "default_engine": "fastparquet",
                  "default_cache": "readahead",
              }
          ),
      )
  
      with ExitStack() as stack:
          for fn, infile in zip(
              fn_rg_map.keys(),
              _open_input_files(
                  list(fn_rg_map.keys()),
                  fs=fs,
                  context_stack=stack,
                  precache_options=precache_options,
                  **open_file_options,
              ),
          ):
              for rg in fn_rg_map[fn]:
                  thislen = rg.num_rows
                  parts = {
                      name: (
                          v
                          if name.endswith("-catdef")
                          else v[start : start + thislen]
                      )
                      for (name, v) in views.items()
                  }
  
                  # Add row-group data to df
                  pf.read_row_group_file(
                      rg,
                      columns,
                      categories,
                      index,
                      assign=parts,
                      partition_meta=pf.partition_meta,
                      infile=infile,
                      **kwargs,
                  )
                  start += thislen
      return df
  
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 11 for calling the FastParquetEngine obj's underlying member method code with example usage: obj.pf_to_pandas(cls, pf, fs, columns, categories, index, open_file_options) and returns: df