class_docstr: ### Description
      Toy net for testing
  
  ### Diagram
  INPUT --> CONV(kernel = 3, activation = relu) --> CONV(kernel = 1, activation = None) --> MULTICLASS OUTPUT
  
  ### Constraints
  None
docstr: :param num_classes: int, number of final output channels
  :param w_initializer: weight initialisation for network
  :param w_regularizer: weight regularisation for network
  :param b_initializer: bias initialisation for network
  :param b_regularizer: bias regularisation for network
  :param acti_func: ctivation function to use
  :param name: layer name
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code: def __init__(
      self,
      num_classes,
      w_initializer=None,
      w_regularizer=None,
      b_initializer=None,
      b_regularizer=None,
      acti_func="prelu",
      name="ToyNet",
  ):
      """
  
      :param num_classes: int, number of final output channels
      :param w_initializer: weight initialisation for network
      :param w_regularizer: weight regularisation for network
      :param b_initializer: bias initialisation for network
      :param b_regularizer: bias regularisation for network
      :param acti_func: ctivation function to use
      :param name: layer name
      """
  
      super(ToyNet, self).__init__(
          num_classes=num_classes,
          w_initializer=w_initializer,
          w_regularizer=w_regularizer,
          b_initializer=b_initializer,
          b_regularizer=b_regularizer,
          acti_func=acti_func,
          name=name,
      )
  
      self.hidden_features = 10
  
imports: from __future__ import absolute_import, print_function
  from niftynet.layer.convolution import ConvolutionalLayer
  from niftynet.network.base_net import BaseNet
label_desc: Use image node_id 1 to create a new ToyNet object from inherited base classes: BaseNet with example: obj = ToyNet(num_classes, w_initializer, w_regularizer, b_initializer, b_regularizer, acti_func, name)