code: def test_datetime_std_with_larger_dataset(axis, skipna, numeric_only):
      num_rows = 250
  
      dt1 = pd.concat(
          [
              pd.Series([pd.NaT] * 15, index=range(15)),
              pd.to_datetime(
                  pd.Series(
                      [
                          datetime.fromtimestamp(
                              1636426704 + (i * 250000)
                          )
                          for i in range(num_rows - 15)
                      ],
                      index=range(15, 250),
                  )
              ),
          ],
          ignore_index=False,
      )
  
      base_numbers = [
          (1638290040706793300 + (i * 69527182702409))
          for i in range(num_rows)
      ]
  
      pdf = pd.DataFrame(
          {"dt1": dt1, "dt2": pd.to_datetime(pd.Series(base_numbers))},
          index=range(250),
      )
  
      for i in range(3, 8):
          pdf[f"dt{i}"] = pd.to_datetime(
              pd.Series([int(x + (0.12 * i)) for x in base_numbers])
          )
  
      ddf = dd.from_pandas(pdf, 8)
  
      kwargs = (
          {} if numeric_only is None else {"numeric_only": numeric_only}
      )
      kwargs["skipna"] = skipna
  
      expected = pdf[["dt1"]].std(axis=axis, **kwargs)
      result = ddf[["dt1"]].std(axis=axis, **kwargs)
      assert_near_timedeltas(result.compute(), expected)
  
      # Same thing but as Series. No axis, since axis=1 raises error
      assert_near_timedeltas(
          ddf["dt1"].std(**kwargs).compute(), pdf["dt1"].std(**kwargs)
      )
  
      # Computation on full dataset
      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 32 for calling a global function with example usage: test_datetime_std_with_larger_dataset(axis, skipna, numeric_only) without return types