def transform(
        cls, input: Boxes, module: Module, param: ParamItem, extra_args: Optional[Dict[str, Any]] = None
    ) -> Boxes:
        """Apply a transformation with respect to the parameters.

        Args:
            input: the input tensor, (B, N, 4, 2) or (B, 4, 2).
            module: any torch Module but only kornia augmentation modules will count
                to apply transformations.
            param: the corresponding parameters to the module.
            extra_args: Optional dictionary of extra arguments with specific options for different input types.
        """
        if extra_args is None:
            extra_args = {}
        _input = input.clone()

        if isinstance(module, (K.GeometricAugmentationBase2D,)):
            _input = module.transform_boxes(
                _input,
                cls.get_instance_module_param(param),
                module.flags,
                transform=module.transform_matrix,
                **extra_args,
            )

        elif isinstance(module, (K.GeometricAugmentationBase3D,)):
            raise NotImplementedError(
                "The support for 3d box operations are not yet supported. You are welcome to file a PR in our repo."
            )

        elif isinstance(module, K.ImageSequential) and not module.is_intensity_only():
            _input = module.transform_boxes(
                _input, params=cls.get_sequential_module_param(param), extra_args=extra_args
            )

        elif isinstance(module, K.container.ImageSequentialBase):
            _input = module.transform_boxes(
                _input, params=cls.get_sequential_module_param(param), extra_args=extra_args
            )

        elif isinstance(module, (K.auto.operations.OperationBase,)):
            return BoxSequentialOps.transform(input, module=module.op, param=param, extra_args=extra_args)

        return _input
Apply a transformation with respect to the parameters.

        Args:
            input: the input tensor, (B, N, 4, 2) or (B, 4, 2).
            module: any torch Module but only kornia augmentation modules will count
                to apply transformations.
            param: the corresponding parameters to the module.
            extra_args: Optional dictionary of extra arguments with specific options for different input types.
        
kornia/augmentation/container/ops.py