code: def arrow_to_pyseries(
      name: str, values: pa.Array, *, rechunk: bool = True
  ) -> PySeries:
      """Construct a PySeries from an Arrow array."""
      array = coerce_arrow(values)
  
      # special handling of empty categorical arrays
      if (
          len(array) == 0
          and isinstance(array.type, pa.DictionaryType)
          and array.type.value_type
          in (
              pa.utf8(),
              pa.large_utf8(),
          )
      ):
          pys = pl.Series(name, [], dtype=Categorical)._s
  
      elif not hasattr(array, "num_chunks"):
          pys = PySeries.from_arrow(name, array)
      else:
          if array.num_chunks > 1:
              # somehow going through ffi with a structarray
              # returns the first chunk everytime
              if isinstance(array.type, pa.StructType):
                  pys = PySeries.from_arrow(
                      name, array.combine_chunks()
                  )
              else:
                  it = array.iterchunks()
                  pys = PySeries.from_arrow(name, next(it))
                  for a in it:
                      pys.append(PySeries.from_arrow(name, a))
          elif array.num_chunks == 0:
              pys = PySeries.from_arrow(name, pa.array([], array.type))
          else:
              pys = PySeries.from_arrow(name, array.chunks[0])
  
          if rechunk:
              pys.rechunk(in_place=True)
  
      return pys
  
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 8 for calling a global function with example usage: arrow_to_pyseries(name, values) and returns: pys