code: def test_dtype() -> None:
      # inferred
      a = pl.Series("a", [[1, 2, 3], [2, 5], [6, 7, 8, 9]])
      assert a.dtype == pl.List
      assert a.dtype.inner == pl.Int64  # type: ignore[attr-defined]
      assert a.dtype.is_(pl.List(pl.Int64))
  
      # explicit
      df = pl.DataFrame(
          data={
              "i": [[1, 2, 3]],
              "tm": [[time(10, 30, 45)]],
              "dt": [[date(2022, 12, 31)]],
              "dtm": [[datetime(2022, 12, 31, 1, 2, 3)]],
          },
          schema=[
              ("i", pl.List(pl.Int8)),
              ("tm", pl.List(pl.Time)),
              ("dt", pl.List(pl.Date)),
              ("dtm", pl.List(pl.Datetime)),
          ],
      )
      assert df.schema == {
          "i": pl.List(pl.Int8),
          "tm": pl.List(pl.Time),
          "dt": pl.List(pl.Date),
          "dtm": pl.List(pl.Datetime),
      }
      assert all(tp.is_nested() for tp in df.dtypes)
      assert df.schema["i"].inner == pl.Int8  # type: ignore[attr-defined]
      assert df.rows() == [
          (
              [1, 2, 3],
              [time(10, 30, 45)],
              [date(2022, 12, 31)],
              [datetime(2022, 12, 31, 1, 2, 3)],
          )
      ]
  
imports: from __future__ import annotations
  import pickle
  from datetime import date, datetime, time
  from typing import TYPE_CHECKING, Any
  import pandas
  import pytest
  import polars
  from polars.testing import assert_frame_equal, assert_series_equal
label_desc: Use image node_id 1 for calling a global function with example usage: test_dtype() without return types