def METHOD_NAME(self):
    # create array
    x = np.arange(100)
    # create a non-contiguous array
    y = x.reshape(10, 10).T
    # run through convert
    # first check to make sure it passes with default
    z = freud.util._convert_array(y, (None, None))
    npt.assert_equal(y.dtype, x.dtype)
    # now change type
    z = freud.util._convert_array(y, (None, None), dtype=np.float32)
    npt.assert_equal(z.dtype, np.float32)
    # now make contiguous
    npt.assert_equal(y.flags.contiguous, False)
    z = freud.util._convert_array(y, (None, None))
    npt.assert_equal(z.flags.contiguous, True)
    # test dimension checking
    with pytest.raises(ValueError):
        z = freud.util._convert_array(y, (None,), dtype=np.float32)
    # test for non-default dtype
    z = freud.util._convert_array(y, dtype=np.float64)
    npt.assert_equal(z.dtype, np.float64)
    # test for list of list input
    yl = [list(r) for r in y]
    zl = freud.util._convert_array(yl, (None, None))
    z = freud.util._convert_array(y, (None, None))
    npt.assert_equal(z, zl)
    # test for dimensions default argument
    zd = freud.util._convert_array(y)
    z = freud.util._convert_array(y, (None, None))
    npt.assert_equal(z, zd)
    # test dimension checking
    with pytest.raises(ValueError):
        z = freud.util._convert_array(y, shape=(1,), dtype=np.float32)
    with pytest.raises(ValueError):
        freud.util._convert_array(z, shape=(None, 9))