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

        Args:
            input: list of input tensors.
            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 = {}
        if isinstance(module, (K.GeometricAugmentationBase2D,)):
            tfm_input = []
            params = cls.get_instance_module_param(param)
            params_i = copy.deepcopy(params)
            for i, inp in enumerate(input):
                params_i["batch_prob"] = params["batch_prob"][i]
                tfm_inp = module.transform_masks(
                    inp, params=params_i, flags=module.flags, transform=module.transform_matrix, **extra_args
                )
                tfm_input.append(tfm_inp)
            input = tfm_input

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

        elif isinstance(module, (_AugmentationBase)):
            tfm_input = []
            params = cls.get_instance_module_param(param)
            params_i = copy.deepcopy(params)
            for i, inp in enumerate(input):
                params_i["batch_prob"] = params["batch_prob"][i]
                tfm_inp = module.transform_masks(inp, params=params_i, flags=module.flags, **extra_args)
                tfm_input.append(tfm_inp)
            input = tfm_input

        elif isinstance(module, K.ImageSequential) and not module.is_intensity_only():
            tfm_input = []
            seq_params = cls.get_sequential_module_param(param)
            for inp in input:
                tfm_inp = module.transform_masks(inp, params=seq_params, extra_args=extra_args)
                tfm_input.append(tfm_inp)
            input = tfm_input

        elif isinstance(module, K.container.ImageSequentialBase):
            tfm_input = []
            seq_params = cls.get_sequential_module_param(param)
            for inp in input:
                tfm_inp = module.transform_masks(inp, params=seq_params, extra_args=extra_args)
                tfm_input.append(tfm_inp)
            input = tfm_input

        elif isinstance(module, (K.auto.operations.OperationBase,)):
            raise NotImplementedError(
                "The support for list of masks under auto operations are not yet supported. You are welcome to file a"
                " PR in our repo."
            )
        return input
Apply a transformation with respect to the parameters.

        Args:
            input: list of input tensors.
            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