How to use the fastr.api.ResourceLimit function in fastr

def create_network(self):
        '''
        Add evaluate components to network.
        '''

        # Create all nodes
        self.node_slicer =\
            self.network.create_node('worc/Slicer:1.0', tool_version='1.0', id='Slicer', resources=ResourceLimit(memory='20G'))

        # Create sinks
        self.sink_PNG =\
            self.network.create_sink('PNGFile', id='PNG')
        self.sink_PNGZoomed =\
            self.network.create_sink('PNGFile', id='PNGZoomed')

        # Create links to sinks
        self.sink_PNG.input = self.node_slicer.outputs['out']
        self.sink_PNGZoomed.input = self.node_slicer.outputs['outzoom']

        # Create sources if not supplied by a WORC network
        if self.mode == 'StandAlone':
            self.source_images = self.network.create_source('ITKImageFile', id='Images')
            self.source_segmentations = self.network.create_source('ITKImageFile', id='Segmentations')

tool_version='0.2',
                                         id='transformix_seg_train_' + label,
                                         resources=ResourceLimit(memory=memory_transformix))

            self.transformix_im_nodes_train[label] =\
                self.network.create_node(transformix_node,
                                         tool_version='0.2',
                                         id='transformix_im_train_' + label,
                                         resources=ResourceLimit(memory=memory_transformix))

            if self.TrainTest:
                self.elastix_nodes_test[label] =\
                    self.network.create_node(elastix_node,
                                             tool_version='0.2',
                                             id='elastix_test_' + label,
                                             resources=ResourceLimit(memory=memory_elastix))

                self.transformix_seg_nodes_test[label] =\
                    self.network.create_node(transformix_node,
                                             tool_version='0.2',
                                             id='transformix_seg_test_' + label,
                                             resources=ResourceLimit(memory=memory_transformix))

                self.transformix_im_nodes_test[label] =\
                    self.network.create_node(transformix_node,
                                             tool_version='0.2',
                                             id='transformix_im_test_' + label,
                                             resources=ResourceLimit(memory=memory_transformix))

            # Create sources_segmentation
            # M1 = moving, others = fixed
            self.elastix_nodes_train[label].inputs['fixed_image'] =\

self.converters_seg_train[label].outputs['image']

        # Input the parameters
        self.nodes_segmentix_train[label].inputs['parameters'] =\
            self.sources_parameters[label].output
        self.sinks_segmentations_segmentix_train[label].input =\
            self.nodes_segmentix_train[label].outputs['segmentation_out']

        if self.TrainTest:
            self.sinks_segmentations_segmentix_test[label] =\
                self.network.create_sink('ITKImageFile',
                                         id='segmentations_out_segmentix_test_' + label)
            self.nodes_segmentix_test[label] =\
                self.network.create_node('segmentix/Segmentix:1.0',
                                         tool_version='1.0',
                                         id='segmentix_test_' + label, resources=ResourceLimit(memory=memory))

            self.nodes_segmentix_test[label].inputs['image'] =\
                self.converters_im_test[label].outputs['image']

            if hasattr(self, 'transformix_seg_nodes_test'):
                if label in self.transformix_seg_nodes_test.keys():
                    # Use output of registration in segmentix
                    self.nodes_segmentix_test[label].inputs['segmentation_in'] =\
                        self.transformix_seg_nodes_test[label].outputs['image']
                else:
                    # Use original segmentation
                    self.nodes_segmentix_test[label].inputs['segmentation_in'] =\
                        self.converters_seg_test[label].outputs['image']
            else:
                # Use original segmentation
                self.nodes_segmentix_test[label].inputs['segmentation_in'] =\

# Assume provided segmentation is on first modality
        if nmod > 0:
            # Use elastix and transformix for registration
            # NOTE: Assume elastix node type is on first configuration
            elastix_node =\
                str(self.configs[0]['General']['RegistrationNode'])

            transformix_node =\
                str(self.configs[0]['General']['TransformationNode'])

            memory_elastix = self.fastr_memory_parameters['Elastix']
            self.elastix_nodes_train[label] =\
                self.network.create_node(elastix_node,
                                         tool_version='0.2',
                                         id='elastix_train_' + label,
                                         resources=ResourceLimit(memory=memory_elastix))

            memory_transformix = self.fastr_memory_parameters['Elastix']
            self.transformix_seg_nodes_train[label] =\
                self.network.create_node(transformix_node,
                                         tool_version='0.2',
                                         id='transformix_seg_train_' + label,
                                         resources=ResourceLimit(memory=memory_transformix))

            self.transformix_im_nodes_train[label] =\
                self.network.create_node(transformix_node,
                                         tool_version='0.2',
                                         id='transformix_im_train_' + label,
                                         resources=ResourceLimit(memory=memory_transformix))

            if self.TrainTest:
                self.elastix_nodes_test[label] =\

tool_version='1.0',
                                     id='decomposition',
                                     resources=ResourceLimit(memory='12G'),
                                     step_id='Evaluation')

        self.node_Ranked_Percentages =\
            self.network.create_node('worc/PlotRankedScores:1.0',
                                     tool_version='1.0',
                                     id='plot_ranked_percentages',
                                     resources=ResourceLimit(memory='20G'),
                                     step_id='Evaluation')
        self.node_Ranked_Posteriors =\
            self.network.create_node('worc/PlotRankedScores:1.0',
                                     tool_version='1.0',
                                     id='plot_ranked_posteriors',
                                     resources=ResourceLimit(memory='20G'),
                                     step_id='Evaluation')
        self.node_Boxplots_Features =\
            self.network.create_node('worc/PlotBoxplotFeatures:1.0',
                                     tool_version='1.0',
                                     id='plot_boxplot_features',
                                     resources=ResourceLimit(memory='12G'),
                                     step_id='Evaluation')

        # Create sinks
        self.sink_ROC_PNG =\
            self.network.create_sink('PNGFile', id='ROC_PNG',
                                     step_id='general_sinks')
        self.sink_ROC_Tex =\
            self.network.create_sink('TexFile', id='ROC_Tex',
                                     step_id='general_sinks')
        self.sink_ROC_CSV =\

message = f'Toolbox {calcfeat_node} not recognized!'
            raise WORCexceptions.WORCKeyError(message)

        self.source_toolbox_name[label] =\
            self.network.create_constant('String', toolbox,
                                         id=f'toolbox_name_{toolbox}_{label}')

        conv_train.inputs['toolbox'] = self.source_toolbox_name[label].output
        conv_train.inputs['config'] = self.sources_parameters[label].output

        if self.TrainTest:
            conv_test =\
                self.network.create_node('worc/FeatureConverter:1.0',
                                         tool_version='1.0',
                                         id='featureconverter_test_' + node_ID,
                                         resources=ResourceLimit(memory='4G'))

            conv_test.inputs['feat_in'] = node_test.outputs['features']
            conv_test.inputs['toolbox'] = self.source_toolbox_name[label].output
            conv_test.inputs['config'] = self.sources_parameters[label].output

        # Append to nodes to list
        self.calcfeatures_train[label].append(node_train)
        self.featureconverter_train[label].append(conv_train)
        if self.TrainTest:
            self.calcfeatures_test[label].append(node_test)
            self.featureconverter_test[label].append(conv_test)

def add_preprocessing(self, preprocess_node, label, nmod):
        """Add nodes required for preprocessing of images."""
        memory = self.fastr_memory_parameters['Preprocessing']
        self.preprocessing_train[label] = self.network.create_node(preprocess_node, tool_version='1.0', id='preprocessing_train_' + label, resources=ResourceLimit(memory=memory))
        if self.TrainTest:
            self.preprocessing_test[label] = self.network.create_node(preprocess_node, tool_version='1.0', id='preprocessing_test_' + label, resources=ResourceLimit(memory=memory))

        # Create required links
        self.preprocessing_train[label].inputs['parameters'] = self.sources_parameters[label].output
        self.preprocessing_train[label].inputs['image'] = self.converters_im_train[label].outputs['image']

        if self.TrainTest:
            self.preprocessing_test[label].inputs['parameters'] = self.sources_parameters[label].output
            self.preprocessing_test[label].inputs['image'] = self.converters_im_test[label].outputs['image']

        if self.metadata_train and len(self.metadata_train) >= nmod + 1:
            self.preprocessing_train[label].inputs['metadata'] = self.sources_metadata_train[label].output

        if self.metadata_test and len(self.metadata_test) >= nmod + 1:
            self.preprocessing_test[label].inputs['metadata'] = self.sources_metadata_test[label].output

        # If there are masks to use in normalization, add them here

def add_ComBat(self):
        """Add ComBat harmonization to the network.

        Note: applied on all objects, not in a train-test or cross-val setting.
        """
        memory = self.fastr_memory_parameters['ComBat']
        self.ComBat =\
            self.network.create_node('combat/ComBat:1.0',
                                     tool_version='1.0',
                                     id='ComBat',
                                     resources=ResourceLimit(memory=memory))

        # Create sink for ComBat output
        self.sinks_features_train_ComBat = self.network.create_sink('HDF5', id='features_train_ComBat')

        # Create links for inputs
        self.link_combat_1 = self.network.create_link(self.source_class_config.output, self.ComBat.inputs['config'])
        self.link_combat_2 = self.network.create_link(self.source_patientclass_train.output, self.ComBat.inputs['patientclass_train'])
        self.link_combat_1.collapse = 'conf'
        self.link_combat_2.collapse = 'pctrain'
        self.links_Combat1_train = dict()
        self.links_Combat1_test = dict()

        # Link Combat output to both sink and classify node
        self.links_Combat_out_train = self.network.create_link(self.ComBat.outputs['features_train_out'], self.classify.inputs['features_train'])
        self.links_Combat_out_train.collapse = 'ComBat'
        self.sinks_features_train_ComBat.input = self.ComBat.outputs['features_train_out']

self.converters_masks_train[label].inputs['image'] = self.sources_masks_train[label].output

                        if self.masks_test and len(self.masks_test) >= nmod + 1:
                            # Create mask source and convert
                            self.sources_masks_test[label] = self.network.create_source('ITKImageFile', id='mask_test_' + label, node_group='test')
                            memory = self.fastr_memory_parameters['WORCCastConvert']
                            self.converters_masks_test[label] = self.network.create_node('worc/WORCCastConvert:0.3.2', tool_version='0.1', id='convert_mask_test_' + label, node_group='test', resources=ResourceLimit(memory=memory))
                            self.converters_masks_test[label].inputs['image'] = self.sources_masks_test[label].output

                        # First convert the images
                        if any(modality in mod for modality in ['MR', 'CT', 'MG', 'PET']):
                            # Use WORC PXCastConvet for converting image formats
                            memory = self.fastr_memory_parameters['WORCCastConvert']
                            self.converters_im_train[label] = self.network.create_node('worc/WORCCastConvert:0.3.2', tool_version='0.1', id='convert_im_train_' + label, resources=ResourceLimit(memory=memory))
                            if self.TrainTest:
                                self.converters_im_test[label] = self.network.create_node('worc/WORCCastConvert:0.3.2', tool_version='0.1', id='convert_im_test_' + label, resources=ResourceLimit(memory=memory))

                        else:
                            raise WORCexceptions.WORCTypeError(('No valid image type for modality {}: {} provided.').format(str(nmod), mod))

                        # Create required links
                        self.converters_im_train[label].inputs['image'] = self.sources_images_train[label].output
                        if self.TrainTest:
                            self.converters_im_test[label].inputs['image'] = self.sources_images_test[label].output

                        # -----------------------------------------------------
                        # Preprocessing
                        preprocess_node = str(self.configs[nmod]['General']['Preprocessing'])
                        print('\t - Adding preprocessing node for image preprocessing.')
                        self.add_preprocessing(preprocess_node, label, nmod)

                        # -----------------------------------------------------