code: def _pandas_series_to_arrow(
      values: pd.Series[Any] | pd.Index[Any],
      *,
      length: int | None = None,
      nan_to_null: bool = True,
  ) -> pa.Array:
      """
      Convert a pandas Series to an Arrow Array.
  
      Parameters
      ----------
      values : :class:`pandas.Series` or :class:`pandas.Index`.
          Series to convert to arrow
      nan_to_null : bool, default = True
          Interpret `NaN` as missing values.
      length : int, optional
          in case all values are null, create a null array of this length.
          if unset, length is inferred from values.
  
      Returns
      -------
      :class:`pyarrow.Array`
  
      """
      dtype = getattr(values, "dtype", None)
      if dtype == "object":
          first_non_none = _get_first_non_none(values.values)  # type: ignore[arg-type]
          if isinstance(first_non_none, str):
              return pa.array(
                  values, pa.large_utf8(), from_pandas=nan_to_null
              )
          elif first_non_none is None:
              return pa.nulls(length or len(values), pa.large_utf8())
          return pa.array(values, from_pandas=nan_to_null)
      elif dtype:
          return pa.array(values, from_pandas=nan_to_null)
      else:
          # Pandas Series is actually a Pandas DataFrame when the original DataFrame
          # contains duplicated columns and a duplicated column is requested with df["a"].
          raise ValueError(
              "duplicate column names found: ",
              f"{values.columns.tolist()!s}",  # type: ignore[union-attr]
          )
  
imports: from __future__ import annotations
  import contextlib
  import warnings
  from datetime import date, datetime, time, timedelta
  from decimal import Decimal
  from functools import lru_cache, singledispatch
  from itertools import islice, zip_longest
  from operator import itemgetter
  from sys import version_info
  from typing import TYPE_CHECKING, Any, Callable, Generator, Iterable, Iterator, Mapping, MutableMapping, Sequence, get_type_hints
  import polars._reexport
  from polars import functions
  from polars.datatypes import INTEGER_DTYPES, N_INFER_DEFAULT, TEMPORAL_DTYPES, Boolean, Categorical, Date, Datetime, Duration, Enum, List, Null, Object, String, Struct, Time, UInt32, Unknown, dtype_to_py_type, is_polars_dtype, numpy_char_code_to_dtype, py_type_to_dtype
  from polars.datatypes.constructor import numpy_type_to_constructor, numpy_values_and_dtype, polars_type_to_constructor, py_type_to_constructor
  from polars.dependencies import _NUMPY_AVAILABLE, _check_for_numpy, _check_for_pandas, _check_for_pydantic, dataclasses, pydantic
  from polars.dependencies import numpy
  from polars.dependencies import pandas
  from polars.dependencies import pyarrow
  from polars.exceptions import ComputeError, ShapeError, TimeZoneAwareConstructorWarning
  from polars.utils._wrap import wrap_df, wrap_s
  from polars.utils.meta import get_index_type, threadpool_size
  from polars.utils.various import _is_generator, arrlen, find_stacklevel, parse_version, range_to_series
label_desc: Use image node_id 16 for calling a global function with example usage: _pandas_series_to_arrow(values) and returns: pa, pa, pa, pa