code: def _nonempty_series(s, idx=None):
      # TODO: Use register dtypes with make_array_nonempty
      if idx is None:
          idx = _nonempty_index(s.index)
      dtype = s.dtype
      if len(s) > 0:
          # use value from meta if provided
          data = [s.iloc[0]] * 2
      elif isinstance(dtype, pd.DatetimeTZDtype):
          entry = pd.Timestamp("1970-01-01", tz=dtype.tz)
          data = [entry, entry]
      elif isinstance(dtype, pd.CategoricalDtype):
          if len(s.cat.categories):
              data = [s.cat.categories[0]] * 2
              cats = s.cat.categories
          else:
              data = _nonempty_index(s.cat.categories)
              cats = s.cat.categories[:0]
          data = pd.Categorical(
              data, categories=cats, ordered=s.cat.ordered
          )
      elif is_integer_na_dtype(dtype):
          data = pd.array([1, None], dtype=dtype)
      elif is_float_na_dtype(dtype):
          data = pd.array([1.0, None], dtype=dtype)
      elif isinstance(dtype, pd.PeriodDtype):
          # pandas 0.24.0+ should infer this to be Series[Period[freq]]
          freq = dtype.freq
          data = [pd.Period("2000", freq), pd.Period("2001", freq)]
      elif isinstance(dtype, pd.SparseDtype):
          entry = _scalar_from_dtype(dtype.subtype)
          data = pd.array([entry, entry], dtype=dtype)
      elif isinstance(dtype, pd.IntervalDtype):
          entry = _scalar_from_dtype(dtype.subtype)
          data = pd.array([entry, entry], dtype=dtype)
      elif type(dtype) in make_array_nonempty._lookup:
          data = make_array_nonempty(dtype)
      else:
          entry = _scalar_from_dtype(dtype)
          data = np.array([entry, entry], dtype=dtype)
  
      out = pd.Series(data, name=s.name, index=idx)
      out.attrs = s.attrs
      return out
  
imports: from __future__ import annotations
  import warnings
  from collections.abc import Iterable
  import numpy
  import pandas
  from pandas.api.types import is_scalar, union_categoricals
  from dask.array.core import Array
  from dask.array.dispatch import percentile_lookup
  from dask.array.percentile import _percentile
  from dask.backends import CreationDispatch, DaskBackendEntrypoint
  from dask.dataframe._compat import is_any_real_numeric_dtype
  from dask.dataframe.core import DataFrame, Index, Scalar, Series, _Frame
  from dask.dataframe.dispatch import categorical_dtype_dispatch, concat, concat_dispatch, from_pyarrow_table_dispatch, get_parallel_type, group_split_dispatch, grouper_dispatch, hash_object_dispatch, is_categorical_dtype_dispatch, make_meta_dispatch, make_meta_obj, meta_lib_from_array, meta_nonempty, partd_encode_dispatch, pyarrow_schema_dispatch, to_pandas_dispatch, to_pyarrow_table_dispatch, tolist_dispatch, union_categoricals_dispatch
  from dask.dataframe.extensions import make_array_nonempty, make_scalar
  from dask.dataframe.utils import _empty_series, _nonempty_scalar, _scalar_from_dtype, is_float_na_dtype, is_integer_na_dtype
  from dask.sizeof import SimpleSizeof, sizeof
  from dask.utils import is_arraylike, is_series_like, typename
label_desc: Use image node_id 14 for calling a global function with example usage: _nonempty_series(s, idx) and returns: out