code: def test_datetime_std_across_axis1_null_results(skipna, numeric_only):
      pdf = pd.DataFrame(
          {
              "dt1": [
                  datetime.fromtimestamp(1636426704 + (i * 250000))
                  for i in range(10)
              ],
              "dt2": [
                  datetime.fromtimestamp(1636426704 + (i * 217790))
                  for i in range(10)
              ],
              "nums": [i for i in range(10)],
          }
      )
  
      ddf = dd.from_pandas(pdf, 3)
  
      kwargs = (
          {} if numeric_only is None else {"numeric_only": numeric_only}
      )
      kwargs["skipna"] = skipna
  
      ctx = contextlib.nullcontext()
      success = True
      if numeric_only is False or (
          PANDAS_GE_200 and numeric_only is None
      ):
          ctx = pytest.raises(TypeError)
          success = False
      elif numeric_only is None:
          ctx = pytest.warns(FutureWarning, match="numeric_only")
  
      # Single column always results in NaT
      expected = pdf[["dt1"]].std(axis=1, **kwargs)
      result = ddf[["dt1"]].std(axis=1, **kwargs)
      if success:
          assert_eq(result, expected)
  
      # Mix of datetimes with other numeric types produces NaNs
      with ctx:
          expected = pdf.std(axis=1, **kwargs)
      with ctx:
          result = ddf.std(axis=1, **kwargs)
      if success:
          assert_eq(result, expected)
  
      # Test with mix of na and truthy datetimes
      pdf2 = pd.DataFrame(
          {
              "dt1": [pd.NaT]
              + [
                  datetime.fromtimestamp(1636426704 + (i * 250000))
                  for i in range(10)
              ]
              + [pd.NaT],
              "dt2": [
                  datetime.fromtimestamp(1636426704 + (i * 250000))
                  for i in range(12)
              ],
              "dt3": [
                  datetime.fromtimestamp(1636426704 + (i * 282616))
                  for i in range(12)
              ],
          }
      )
  
      ddf2 = dd.from_pandas(pdf2, 3)
  
      expected = pdf2.std(axis=1, **kwargs)
      result = ddf2.std(axis=1, **kwargs)
      if success:
          assert_eq(result, expected)
  
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 33 for calling a global function with example usage: test_datetime_std_across_axis1_null_results(skipna, numeric_only) without return types