How to use the fastr.config.mounts function in fastr

self.elastix_toolname = 'Elastix'
        self.transformix_toolname = 'Transformix'

        # self.Elastix = sitk.SimpleElastix()
        self.create_network('pairwise')
        self.FixedImage = []
        self.MovingImage = []
        self.FixedMask = []
        self.MovingMask = []
        self.ToTransform = []
        self.ParameterMaps = []  # sitk.VectorOfParameterMap()

        self.TransformedImage = 'vfs://tmp/WORC_Elastix/results/elastix_output_image_{sample_id}_{cardinality}.nii.gz'
        self.TransformedSeg = 'vfs://tmp/WORC_Elastix/results/elastix_output_seg_{sample_id}_{cardinality}.nii.gz'
        self.TransformParameters = 'vfs://tmp/WORC_Elastix/results/elastix_output_trans_{sample_id}_{cardinality}.txt'
        self.fastr_tmpdir = os.path.join(fastr.config.mounts['tmp'], 'WORC_Elastix')
        # TODO: Add initial transformation

----------

        network: fastr network, default None
                If you input a network, the evaluate network is added
                to the existing network.

        '''
        if network is not None:
            self.network = network
            self.mode = 'WORC'
        else:
            self.mode = 'StandAlone'
            self.fastr_plugin = fastr_plugin
            self.name = 'WORC_Slicer_' + name
            self.network = fastr.create_network(id=self.name)
            self.fastr_tmpdir = os.path.join(fastr.config.mounts['tmp'], self.name)

        if images is None and self.mode == 'StandAlone':
            message = 'Either images and segmentations as input or a WORC' +\
             'network is required for the Evaluate network.'
            raise WORCexceptions.WORCIOError(message)

        self.images = images
        self.segmentations = segmentations

        self.create_network()

def execute(self):
        # this function is kind of like the build()-function in a builder, except it peforms execute on the object being built as well
        self._validate()  # do some final sanity checking before we execute the thing

        if self._radiomix_feature_file:
            # Convert radiomix features and use those as inputs
            output_folder = os.path.join(fastr.config.mounts['tmp'],
                                         'Radiomix_features')

            # Check if output folder exists: otherwise create
            if not os.path.exists(output_folder):
                os.mkdir(output_folder)

            # convert the features
            convert_radiomix_features(self._radiomix_feature_file, output_folder)

            # Set the newly created feature files as the WORC input
            self.features_from_this_directory(output_folder)

        # Training sources
        if self.images_train:
            self._worc.images_train = self.images_train
        elif self._images_train:

def execute(self):
        # this function is kind of like the build()-function in a builder, except it peforms execute on the object being built as well
        self._validate()  # do some final sanity checking before we execute the thing

        if self._radiomix_feature_file:
            # Convert radiomix features and use those as inputs
            output_folder = os.path.join(fastr.config.mounts['tmp'],
                                         'Radiomix_features')

            # Check if output folder exists: otherwise create
            if not os.path.exists(output_folder):
                os.mkdir(output_folder)

            # convert the features
            convert_radiomix_features(self._radiomix_feature_file, output_folder)

            # Set the newly created feature files as the WORC input
            self.features_from_this_directory(output_folder)

        self._worc.images_train = self._images_train
        self._worc.features_train = self._features_train
        self._worc.segmentations_train = self._segmentations_train

fitandscore.inputs['traintest'] = traintest_data.output
        fitandscore.inputs['parameters'] = parameter_data.output
        sink_output.input = fitandscore.outputs['fittedestimator']

        source_data = {'estimator_source': estimatordata,
                       'traintest': traintest_files,
                       'parameters': parameter_files}
        sink_data = {'output': f"vfs://tmp/GS/{name}/output_{{sample_id}}_{{cardinality}}{{ext}}"}

        network.execute(source_data, sink_data,
                        tmpdir=os.path.join(tempfolder, 'tmp'),
                        execution_plugin=self.fastr_plugin)

        # Read in the output data once finished
        # TODO: expanding fastr url is probably a nicer way
        sink_files = glob.glob(os.path.join(fastr.config.mounts['tmp'], 'GS', name) + '/output*.hdf5')
        save_data = list()
        for output in sink_files:
            data = pd.read_hdf(output)
            save_data.extend(list(data['RET']))

        # if one choose to see train score, "out" will contain train score info
        try:
            if self.return_train_score:
                (train_scores, test_scores, test_sample_counts,
                 fit_time, score_time, parameters_est, parameters_all) =\
                  zip(*save_data)
            else:
                (test_scores, test_sample_counts,
                 fit_time, score_time, parameters_est, parameters_all) =\
                  zip(*save_data)
        except ValueError as e:

self.segmentations_test = list()
        self.semantics_test = list()
        self.labels_test = list()
        self.masks_test = list()
        self.masks_normalize_test = list()
        self.features_test = list()
        self.metadata_test = list()

        self.Elastix_Para = list()
        self.label_names = 'Label1, Label2'

        # Set some defaults, name
        self.fastr_plugin = 'LinearExecution'
        if name == '':
            name = [randint(0, 9) for p in range(0, 5)]
        self.fastr_tmpdir = os.path.join(fastr.config.mounts['tmp'], self.name)

        self.additions = dict()
        self.CopyMetadata = True
        self.segmode = []
        self._add_evaluation = False
        self.TrainTest = False

        # Memory settings for all fastr nodes
        self.fastr_memory_parameters = dict()
        self.fastr_memory_parameters['FeatureCalculator'] = '14G'
        self.fastr_memory_parameters['Classification'] = '12G'
        self.fastr_memory_parameters['WORCCastConvert'] = '4G'
        self.fastr_memory_parameters['Preprocessing'] = '4G'
        self.fastr_memory_parameters['Elastix'] = '4G'
        self.fastr_memory_parameters['Transformix'] = '4G'
        self.fastr_memory_parameters['Segmentix'] = '6G'

If you input a network, the evaluate network is added
                to the existing network.

        """
        if parent is not None:
            self.parent = parent
            self.network = parent.network
            self.mode = 'WORC'
            self.name = parent.network.id
            self.ensemble = parent.configs[0]['Ensemble']['Use']
        else:
            self.mode = 'StandAlone'
            self.fastr_plugin = fastr_plugin
            self.name = 'WORC_Evaluate_' + name
            self.network = fastr.create_network(id=self.name)
            self.fastr_tmpdir = os.path.join(fastr.config.mounts['tmp'], self.name)
            self.ensemble = ensemble

        if features is None and self.mode == 'StandAlone':
            raise WORCexceptions.WORCIOError('Either features as input or a WORC network is required for the Evaluate network.')

        self.features = features

        self.label_type = label_type

        self.create_network()