code: def read_metadata(
      cls,
      fs,
      paths,
      categories=None,
      index=None,
      use_nullable_dtypes=None,
      dtype_backend=None,
      gather_statistics=None,
      filters=None,
      split_row_groups="adaptive",
      blocksize=None,
      aggregate_files=None,
      ignore_metadata_file=False,
      metadata_task_size=None,
      parquet_file_extension=None,
      **kwargs,
  ):
      if use_nullable_dtypes is not None:
          raise ValueError(
              "`use_nullable_dtypes` is not supported by the fastparquet engine"
          )
      if dtype_backend is not None:
          raise ValueError(
              "`dtype_backend` is not supported by the fastparquet engine"
          )
  
      # Stage 1: Collect general dataset information
      dataset_info = cls._collect_dataset_info(
          paths,
          fs,
          categories,
          index,
          gather_statistics,
          filters,
          split_row_groups,
          blocksize,
          aggregate_files,
          ignore_metadata_file,
          metadata_task_size,
          parquet_file_extension,
          kwargs,
      )
  
      # Stage 2: Generate output `meta`
      meta = cls._create_dd_meta(dataset_info)
  
      # Stage 3: Generate parts and stats
      parts, stats, common_kwargs = cls._construct_collection_plan(
          dataset_info
      )
  
      # Cannot allow `None` in columns if the user has specified index=False
      index = dataset_info["index"]
      if index is False and None in meta.columns:
          meta.drop(columns=[None], inplace=True)
  
      # Add `common_kwargs` to the first element of `parts`.
      # We can return as a separate element in the future, but
      # should avoid breaking the API for now.
      if len(parts):
          parts[0]["common_kwargs"] = common_kwargs
          parts[0]["aggregation_depth"] = dataset_info[
              "aggregation_depth"
          ]
          parts[0]["split_row_groups"] = dataset_info[
              "split_row_groups"
          ]
  
      if len(parts) and len(parts[0]["piece"]) == 1:
          # Strip all partition-dependent or unnecessary
          # data from the `ParquetFile` object
          pf = dataset_info["pf"]
          pf.row_groups = None
          pf.fmd.row_groups = None
          pf._statistics = None
          parts[0]["common_kwargs"]["parquet_file"] = pf
  
      return (meta, stats, parts, index)
  
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 8 for calling the FastParquetEngine obj's underlying member method code with example usage: obj.read_metadata(cls, fs, paths, categories, index, use_nullable_dtypes, dtype_backend, gather_statistics, filters, split_row_groups, blocksize, aggregate_files, ignore_metadata_file, metadata_task_size, parquet_file_extension) and returns: meta,  stats,  parts,  index