code: def process_statistics(
      parts,
      statistics,
      filters,
      index,
      blocksize,
      split_row_groups,
      fs,
      aggregation_depth,
  ):
      """Process row-group column statistics in metadata
      Used in read_parquet.
      """
      if statistics and len(parts) != len(statistics):
          # It is up to the Engine to guarantee that these
          # lists are the same length (if statistics are defined).
          # This misalignment may be indicative of a bug or
          # incorrect read_parquet usage, so throw a warning.
          warnings.warn(
              f"Length of partition statistics ({len(statistics)}) "
              f"does not match the partition count ({len(parts)}). "
              f"This may indicate a bug or incorrect read_parquet "
              f"usage. We must ignore the statistics and disable: "
              f"filtering, divisions, and/or file aggregation."
          )
          statistics = []
  
      divisions = None
      if statistics:
          result = list(
              zip(
                  *[
                      (part, stats)
                      for part, stats in zip(parts, statistics)
                      if stats["num-rows"] > 0
                  ]
              )
          )
          parts, statistics = result or [[], []]
          if filters:
              parts, statistics = apply_filters(
                  parts, statistics, filters
              )
  
          # Aggregate parts/statistics if we are splitting by row-group
          if blocksize or (
              split_row_groups and int(split_row_groups) > 1
          ):
              parts, statistics = aggregate_row_groups(
                  parts,
                  statistics,
                  blocksize,
                  split_row_groups,
                  fs,
                  aggregation_depth,
              )
  
          # Convert str index to list
          index = [index] if isinstance(index, str) else index
  
          # TODO: Remove `filters` criteria below after deprecation cycle.
          # We can then remove the `sorted_col_names` logic and warning.
          # See: https://github.com/dask/dask/pull/9661
          process_columns = index if index and len(index) == 1 else None
          if filters:
              process_columns = None
  
          # Use statistics to define divisions
          if process_columns or filters:
              sorted_col_names = []
              for sorted_column_info in sorted_columns(
                  statistics, columns=process_columns
              ):
                  if index and sorted_column_info["name"] in index:
                      divisions = sorted_column_info["divisions"]
                      break
                  else:
                      # Filtered columns may also be sorted
                      sorted_col_names.append(
                          sorted_column_info["name"]
                      )
  
              if index is None and sorted_col_names:
                  assert bool(
                      filters
                  )  # Should only get here when filtering
                  warnings.warn(
                      f"Sorted columns detected: {sorted_col_names}\n"
                      f"Use the `index` argument to set a sorted column as your "
                      f"index to create a DataFrame collection with known `divisions`.",
                      UserWarning,
                  )
  
      divisions = divisions or (None,) * (len(parts) + 1)
      return parts, divisions, index
  
imports: from __future__ import annotations
  import contextlib
  import math
  import warnings
  from typing import Literal
  import pandas
  import tlz
  from fsspec.core import get_fs_token_paths
  from fsspec.utils import stringify_path
  import dask
  from dask.base import tokenize
  from dask.blockwise import BlockIndex
  from dask.dataframe.backends import dataframe_creation_dispatch
  from dask.dataframe.core import DataFrame, Scalar
  from dask.dataframe.io.io import from_map
  from dask.dataframe.io.parquet.utils import Engine, _sort_and_analyze_paths, _split_user_options
  from dask.dataframe.io.utils import DataFrameIOFunction, _is_local_fs
  from dask.dataframe.methods import concat
  from dask.delayed import Delayed
  from dask.highlevelgraph import HighLevelGraph
  from dask.layers import DataFrameIOLayer
  from dask.utils import apply, import_required, natural_sort_key, parse_bytes
label_desc: Use image node_id 9 for calling a global function with example usage: process_statistics(parts, statistics, filters, index, blocksize, split_row_groups, fs, aggregation_depth) and returns: parts,  divisions,  index