code: def test_count_numeric_only_axis_one():
      df = pd.DataFrame(
          {
              "int": [1, 2, 3, 4, 5, 6, 7, 8],
              "float": [1.0, 2.0, 3.0, 4.0, np.nan, 6.0, 7.0, 8.0],
              "dt": [pd.NaT]
              + [datetime(2011, i, 1) for i in range(1, 8)],
              "str": list("abcdefgh"),
              "timedelta": pd.to_timedelta(
                  [1, 2, 3, 4, 5, 6, 7, np.nan]
              ),
              "bool": [True, False] * 4,
          }
      )
      ddf = dd.from_pandas(df, npartitions=2)
  
      assert_eq(ddf.count(axis=1), df.count(axis=1))
      assert_eq(
          ddf.count(numeric_only=False, axis=1),
          df.count(numeric_only=False, axis=1),
      )
      assert_eq(
          ddf.count(numeric_only=True, axis=1),
          df.count(numeric_only=True, axis=1),
      )
  
imports: from __future__ import annotations
  import contextlib
  import warnings
  from datetime import datetime
  import numpy
  import pandas
  import pytest
  from pandas.api.types import is_scalar
  import dask.dataframe
  from dask.array.numpy_compat import _numpy_125
  from dask.dataframe._compat import PANDAS_GE_140, PANDAS_GE_150, PANDAS_GE_200, PANDAS_VERSION, check_numeric_only_deprecation
  from dask.dataframe.utils import assert_dask_graph, assert_eq, make_meta, pyarrow_strings_enabled
label_desc: Use image node_id 19 for calling a global function with example usage: test_count_numeric_only_axis_one() without return types