Remove deprecated allow_lazy

[dcherian]

No description provided.

[dcherian]

Upstream dev failures look unrelated to this PR. Seems to be from dask's meta inference

=================================== FAILURES ===================================
________________ test_argmin_max[x-False-min-True-False-str-1] _________________

dim_num = 1, dtype = <class 'str'>, contains_nan = False, dask = True
func = 'min', skipna = False, aggdim = 'x'

    @pytest.mark.parametrize("dim_num", [1, 2])
    @pytest.mark.parametrize("dtype", [float, int, np.float32, np.bool_, str])
    @pytest.mark.parametrize("contains_nan", [True, False])
    @pytest.mark.parametrize("dask", [False, True])
    @pytest.mark.parametrize("func", ["min", "max"])
    @pytest.mark.parametrize("skipna", [False, True])
    @pytest.mark.parametrize("aggdim", ["x", "y"])
    def test_argmin_max(dim_num, dtype, contains_nan, dask, func, skipna, aggdim):
        # pandas-dev/pandas#16830, we do not check consistency with pandas but
        # just make sure da[da.argmin()] == da.min()
    
        if aggdim == "y" and dim_num < 2:
            pytest.skip("dim not in this test")
    
        if dask and not has_dask:
            pytest.skip("requires dask")
    
        if contains_nan:
            if not skipna:
                pytest.skip(
                    "numpy's argmin (not nanargmin) does not handle " "object-dtype"
                )
            if skipna and np.dtype(dtype).kind in "iufc":
                pytest.skip("numpy's nanargmin raises ValueError for all nan axis")
        da = construct_dataarray(dim_num, dtype, contains_nan=contains_nan, dask=dask)
    
        with warnings.catch_warnings():
            warnings.filterwarnings("ignore", "All-NaN slice")
    
            actual = da.isel(
                **{aggdim: getattr(da, "arg" + func)(dim=aggdim, skipna=skipna).compute()}
            )
            expected = getattr(da, func)(dim=aggdim, skipna=skipna)
>           assert_allclose(
    
        if dtype and meta.dtype != dtype:
>           meta = meta.astype(dtype)
E           ValueError: invalid literal for int() with base 10: ''

[mathause]

Yes I think the failures are unrelated (#4502)