code: def __init__(self, net_param, action_param, action):
      BaseApplication.__init__(self)
      tf.logging.info("starting recursive regression application")
      self.action = action
  
      self.net_param = net_param
      self.net2_param = copy.deepcopy(net_param)
      self.action_param = action_param
      self.regression_param = None
  
      self.data_param = None
      self.SUPPORTED_SAMPLING = {
          "uniform": (
              self.initialise_uniform_sampler,
              self.initialise_grid_sampler,
              self.initialise_grid_aggregator,
          ),
          "weighted": (
              self.initialise_weighted_sampler,
              self.initialise_grid_sampler,
              self.initialise_grid_aggregator,
          ),
          "resize": (
              self.initialise_resize_sampler,
              self.initialise_resize_sampler,
              self.initialise_resize_aggregator,
          ),
      }
  
imports: import tensorflow
  import copy
  from niftynet.application.base_application import BaseApplication
  from niftynet.engine.application_factory import ApplicationNetFactory
  from niftynet.engine.application_factory import OptimiserFactory
  from niftynet.engine.application_variables import CONSOLE
  from niftynet.engine.application_variables import NETWORK_OUTPUT
  from niftynet.engine.application_variables import TF_SUMMARIES
  from niftynet.engine.sampler_grid_v2 import GridSampler
  from niftynet.engine.sampler_resize_v2 import ResizeSampler
  from niftynet.engine.sampler_uniform_v2 import UniformSampler
  from niftynet.engine.sampler_weighted_v2 import WeightedSampler
  from niftynet.engine.windows_aggregator_grid import GridSamplesAggregator
  from niftynet.engine.windows_aggregator_resize import ResizeSamplesAggregator
  from niftynet.io.image_reader import ImageReader
  from niftynet.layer.crop import CropLayer
  from niftynet.layer.histogram_normalisation import HistogramNormalisationLayer
  from niftynet.layer.loss_regression import LossFunction
  from niftynet.layer.mean_variance_normalisation import MeanVarNormalisationLayer
  from niftynet.layer.pad import PadLayer
  from niftynet.layer.post_processing import PostProcessingLayer
  from niftynet.layer.rand_flip import RandomFlipLayer
  from niftynet.layer.rand_rotation import RandomRotationLayer
  from niftynet.layer.rand_spatial_scaling import RandomSpatialScalingLayer
label_desc: Use image node_id 1 to create a new RegressionRecApplication object from inherited base classes: BaseApplication with example: obj = RegressionRecApplication(net_param, action_param, action)