code: def dataframe_to_types(df, preserve_index, columns=None):
      (
          all_names,
          column_names,
          _,
          index_descriptors,
          index_columns,
          columns_to_convert,
          _,
      ) = _get_columns_to_convert(df, None, preserve_index, columns)
  
      types = []
      # If pandas knows type, skip conversion
      for c in columns_to_convert:
          values = c.values
          if _pandas_api.is_categorical(values):
              type_ = pa.array(c, from_pandas=True).type
          elif _pandas_api.is_extension_array_dtype(values):
              empty = (
                  c.head(0)
                  if isinstance(c, _pandas_api.pd.Series)
                  else c[:0]
              )
              type_ = pa.array(empty, from_pandas=True).type
          else:
              values, type_ = get_datetimetz_type(values, c.dtype, None)
              type_ = pa.lib._ndarray_to_arrow_type(values, type_)
              if type_ is None:
                  type_ = pa.array(c, from_pandas=True).type
          types.append(type_)
  
      metadata = construct_metadata(
          columns_to_convert,
          df,
          column_names,
          index_columns,
          index_descriptors,
          preserve_index,
          types,
      )
  
      return all_names, types, metadata
  
imports: import ast
  from collections.abc import Sequence
  from concurrent import futures
  import concurrent.futures.thread
  from copy import deepcopy
  from itertools import zip_longest
  import json
  import operator
  import re
  import warnings
  import numpy
  import pyarrow
  from pyarrow.lib import _pandas_api, frombytes
label_desc: Use image node_id 17 for calling a global function with example usage: dataframe_to_types(df, preserve_index, columns) and returns: all_names,  types,  metadata