code: def _get_non_empty_data(s):
      if isinstance(s, cudf.core.column.CategoricalColumn):
          categories = (
              s.categories
              if len(s.categories)
              else [UNKNOWN_CATEGORIES]
          )
          codes = cudf.core.column.full(
              size=2,
              fill_value=0,
              dtype=cudf._lib.types.size_type_dtype,
          )
          ordered = s.ordered
          data = cudf.core.column.build_categorical_column(
              categories=categories, codes=codes, ordered=ordered
          )
      elif isinstance(s, cudf.core.column.ListColumn):
          leaf_type = s.dtype.leaf_type
          if is_string_dtype(leaf_type):
              data = ["cat", "dog"]
          else:
              data = np.array([0, 1], dtype=leaf_type).tolist()
          data = _nest_list_data(data, s.dtype) * 2
          data = cudf.core.column.as_column(data, dtype=s.dtype)
      elif isinstance(s, cudf.core.column.StructColumn):
          struct_dtype = s.dtype
          data = [{key: None for key in struct_dtype.fields.keys()}] * 2
          data = cudf.core.column.as_column(data, dtype=s.dtype)
      elif is_string_dtype(s.dtype):
          data = pa.array(["cat", "dog"])
      elif is_datetime64tz_dtype(s.dtype):
          from cudf.utils.dtypes import get_time_unit
  
          data = cudf.date_range(
              "2001-01-01", periods=2, freq=get_time_unit(s)
          )
          data = data.tz_localize(str(s.dtype.tz))._column
      else:
          if pd.api.types.is_numeric_dtype(s.dtype):
              data = cudf.core.column.as_column(
                  cp.arange(start=0, stop=2, dtype=s.dtype)
              )
          else:
              data = cudf.core.column.as_column(
                  cp.arange(start=0, stop=2, dtype="int64")
              ).astype(s.dtype)
      return data
  
imports: import warnings
  from collections.abc import Iterator
  import cupy
  import numpy
  import pandas
  import pyarrow
  from pandas.api.types import is_scalar
  from pandas.core.tools.datetimes import is_datetime64tz_dtype
  import dask.dataframe
  from dask import config
  from dask.array.dispatch import percentile_lookup
  from dask.dataframe.backends import DataFrameBackendEntrypoint, PandasBackendEntrypoint
  from dask.dataframe.core import get_parallel_type, meta_nonempty
  from dask.dataframe.dispatch import categorical_dtype_dispatch, concat_dispatch, from_pyarrow_table_dispatch, group_split_dispatch, grouper_dispatch, hash_object_dispatch, is_categorical_dtype_dispatch, make_meta_dispatch, pyarrow_schema_dispatch, to_pyarrow_table_dispatch, tolist_dispatch, union_categoricals_dispatch
  from dask.dataframe.utils import UNKNOWN_CATEGORIES, _nonempty_scalar, _scalar_from_dtype, make_meta_obj
  from dask.sizeof import sizeof
  from dask.utils import Dispatch, is_arraylike
  import cudf
  from cudf.api.types import is_string_dtype
  from cudf.utils.nvtx_annotation import _dask_cudf_nvtx_annotate
  from .core import DataFrame, Index, Series
label_desc: Use image node_id 3 for calling a global function with example usage: _get_non_empty_data(s) and returns: data