docstr: :param num_features: int, number of features
  :param w_initializer: weight initialisation for network
  :param w_regularizer: weight regularisation for network
  :param num_classes: int, number of prediction channels
  :param acti_func: activation function to use
  :param name: layer name
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code: def __init__(
      self,
      num_features=None,
      w_initializer=None,
      w_regularizer=None,
      num_classes=1,
      acti_func="elu",
      name="ScoreLayer",
  ):
      """
  
      :param num_features: int, number of features
      :param w_initializer: weight initialisation for network
      :param w_regularizer: weight regularisation for network
      :param num_classes: int, number of prediction channels
      :param acti_func: activation function to use
      :param name: layer name
      """
      super(ScoreLayer, self).__init__(name=name)
      self.num_classes = num_classes
      self.acti_func = acti_func
      self.num_features = num_features
      self.n_layers = len(self.num_features)
      self.initializers = {"w": w_initializer}
      self.regularizers = {"w": w_regularizer}
  
imports: from __future__ import absolute_import, print_function
  import tensorflow
  from niftynet.layer.base_layer import TrainableLayer
  from niftynet.layer.convolution import ConvLayer
  from niftynet.layer.convolution import ConvolutionalLayer
  from niftynet.layer.dilatedcontext import DilatedTensor
  from niftynet.layer.downsample import DownSampleLayer
  from niftynet.layer.upsample import UpSampleLayer
  from niftynet.network.base_net import BaseNet
  from niftynet.network.highres3dnet import HighResBlock
label_desc: Use image node_id 1 to create a new ScoreLayer object from inherited base classes: TrainableLayer with example: obj = ScoreLayer(num_features, w_initializer, w_regularizer, num_classes, acti_func, name)