code: def test_series_fillna_numerical(
      psr, data_dtype, fill_value, inplace
  ):
      test_psr = psr.copy(deep=True)
      # TODO: These tests should use Pandas' nullable int type
      # when we support a recent enough version of Pandas
      # https://pandas.pydata.org/pandas-docs/stable/user_guide/integer_na.html
      if (
          np.dtype(data_dtype).kind not in ("f")
          and test_psr.dtype.kind == "i"
      ):
          test_psr = test_psr.astype(
              cudf.utils.dtypes.np_dtypes_to_pandas_dtypes[
                  np.dtype(data_dtype)
              ]
          )
  
      gsr = cudf.from_pandas(test_psr)
  
      if isinstance(fill_value, pd.Series):
          fill_value_cudf = cudf.from_pandas(fill_value)
      else:
          fill_value_cudf = fill_value
  
      expected = test_psr.fillna(fill_value, inplace=inplace)
      actual = gsr.fillna(fill_value_cudf, inplace=inplace)
  
      if inplace:
          expected = test_psr
          actual = gsr
  
      # TODO: Remove check_dtype when we have support
      # to compare with pandas nullable dtypes
      assert_eq(expected, actual, check_dtype=False)
  
imports: import re
  from decimal import Decimal
  import numpy
  import pandas
  import pytest
  import cudf
  from cudf.core._compat import PANDAS_GE_134, PANDAS_GE_150
  from cudf.core.dtypes import Decimal32Dtype, Decimal64Dtype, Decimal128Dtype
  from cudf.testing._utils import INTEGER_TYPES, NUMERIC_TYPES, assert_eq, assert_exceptions_equal
label_desc: Use image node_id 6 for calling a global function with example usage: test_series_fillna_numerical(psr, data_dtype, fill_value, inplace) without return types