dev(narugo): much larger abs

            image,

            repo_id='deepghs/timms_mobilenet',

            model_name='mobilenetv4_hybrid_medium.ix_e550_r384_in1k',

            # label_group='descriptions',

            # label_group='definitions',

            # topk=None,

        )

        assert scores == pytest.approx({

            'n02966687': 0.48493319749832153,

            'n04517823': 0.0027278559282422066,

            'n03126707': 0.0026790976990014315,

            'n02879718': 0.0026228304486721754

        })

        }, abs=1e-3)

    def test_classify_predict_score_group1(self):

        image = Image.open(get_testfile('png_640.png'))

            'vacuum, vacuum cleaner': 0.0027278559282422066,

            'crane': 0.0026790976990014315,

            'bow': 0.0026228304486721754

        })

        }, abs=1e-3)

    def test_classify_predict_score_group2(self):

        image = Image.open(get_testfile('png_640.png'))

            'an electrical home appliance that cleans by suction': 0.0027278559282422066,

            'lifts and moves heavy objects; lifting tackle is suspended from a pivoted boom that rotates around a vertical axis': 0.0026790976990014315,

            'a weapon for shooting arrows, composed of a curved piece of resilient wood with a taut cord to propel the arrow': 0.0026228304486721754

        })

        }, abs=1e-3)

    def test_classify_predict_score_top5(self):

        image = Image.open(get_testfile('png_640.png'))

            'n04482393': 0.07170269638299942,

            'n04154565': 0.029927952215075493,

            'n03000684': 0.02070867270231247,

        })

        }, abs=1e-3)

    def test_classify_predict_score_top5_group1(self):

        image = Image.open(get_testfile('png_640.png'))

            'tricycle, trike, velocipede': 0.07170269638299942,

            'screwdriver': 0.029927952215075493,

            'chain saw, chainsaw': 0.02070867270231247,

        })

        }, abs=1e-3)