def default_transforms():
        """Default transforms for imgaug.
        Implement RandAugment by imgaug.
        Plase visit `https://arxiv.org/abs/1909.13719` for more information.
        Augmenters and hyper parameters are borrowed from the following repo:
        https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/autoaugment.py # noqa
        Miss one augmenter ``SolarizeAdd`` since imgaug doesn't support this.
        Returns:
            dict: The constructed RandAugment transforms.
        """
        # RandAugment hyper params
        num_augmenters = 2
        cur_magnitude, max_magnitude = 9, 10
        cur_level = 1.0 * cur_magnitude / max_magnitude

        return [
            dict(
                type='SomeOf',
                n=num_augmenters,
                children=[
                    dict(
                        type='ShearX',
                        shear=17.19 * cur_level * random.choice([-1, 1])),
                    dict(
                        type='ShearY',
                        shear=17.19 * cur_level * random.choice([-1, 1])),
                    dict(
                        type='TranslateX',
                        percent=.2 * cur_level * random.choice([-1, 1])),
                    dict(
                        type='TranslateY',
                        percent=.2 * cur_level * random.choice([-1, 1])),
                    dict(
                        type='Rotate',
                        rotate=30 * cur_level * random.choice([-1, 1])),
                    dict(type='Posterize', nb_bits=max(1, int(4 * cur_level))),
                    dict(type='Solarize', threshold=256 * cur_level),
                    dict(type='EnhanceColor', factor=1.8 * cur_level + .1),
                    dict(type='EnhanceContrast', factor=1.8 * cur_level + .1),
                    dict(
                        type='EnhanceBrightness', factor=1.8 * cur_level + .1),
                    dict(type='EnhanceSharpness', factor=1.8 * cur_level + .1),
                    dict(type='Autocontrast', cutoff=0),
                    dict(type='Equalize'),
                    dict(type='Invert', p=1.),
                    dict(
                        type='Cutout',
                        nb_iterations=1,
                        size=0.2 * cur_level,
                        squared=True)
                ])
        ]
Default transforms for imgaug.
        Implement RandAugment by imgaug.
        Plase visit `https://arxiv.org/abs/1909.13719` for more information.
        Augmenters and hyper parameters are borrowed from the following repo:
        https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/autoaugment.py # noqa
        Miss one augmenter ``SolarizeAdd`` since imgaug doesn't support this.
        Returns:
            dict: The constructed RandAugment transforms.
        
EVA-01/video/datasets/pipeline.py