code: def test_datetime_std_creates_copy_cols(axis, numeric_only):
      pdf = pd.DataFrame(
          {
              "dt1": [
                  datetime.fromtimestamp(1636426700 + (i * 250000))
                  for i in range(10)
              ],
              "dt2": [
                  datetime.fromtimestamp(1636426700 + (i * 300000))
                  for i in range(10)
              ],
          }
      )
  
      ddf = dd.from_pandas(pdf, 3)
  
      kwargs = (
          {} if numeric_only is None else {"numeric_only": numeric_only}
      )
  
      # Series test (same line twice to make sure data structure wasn't mutated)
      assert_eq(ddf["dt1"].std(**kwargs), pdf["dt1"].std(**kwargs))
      assert_eq(ddf["dt1"].std(**kwargs), pdf["dt1"].std(**kwargs))
  
      # DataFrame test (same line twice to make sure data structure wasn't mutated)
      expected = pdf.std(axis=axis, **kwargs)
      result = ddf.std(axis=axis, **kwargs)
      assert_near_timedeltas(result.compute(), expected)
  
      expected = pdf.std(axis=axis, **kwargs)
      result = ddf.std(axis=axis, **kwargs)
      assert_near_timedeltas(result.compute(), 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 31 for calling a global function with example usage: test_datetime_std_creates_copy_cols(axis, numeric_only) without return types