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 images: tensor, input to the network
  :param is_training: boolean, True if network is in training mode
  :param unused_kwargs: other arguments, not in use
  :return: tensor, network output
docstr_tok: [":","param","images",":","tensor",",","input","to","the","network",":","param","is_training",":","boolean",",","True","if","network","is","in","training","mode",":","param","unused_kwargs",":","other","arguments",",","not","in","use",":","return",":","tensor",",","network","output"]
code: def layer_op(self, images, is_training=True, **unused_kwargs):
      """
  
      :param images: tensor, input to the network
      :param is_training: boolean, True if network is in training mode
      :param unused_kwargs: other arguments, not in use
      :return: tensor, network output
      """
      conv_1 = ConvolutionalLayer(
          self.hidden_features,
          kernel_size=3,
          w_initializer=self.initializers["w"],
          w_regularizer=self.regularizers["w"],
          b_initializer=self.initializers["b"],
          b_regularizer=self.regularizers["b"],
          acti_func="relu",
          name="conv_input",
      )
  
      conv_2 = ConvolutionalLayer(
          self.num_classes,
          kernel_size=1,
          w_initializer=self.initializers["w"],
          w_regularizer=self.regularizers["w"],
          b_initializer=self.initializers["b"],
          b_regularizer=self.regularizers["b"],
          acti_func=None,
          name="conv_output",
      )
  
      flow = conv_1(images, is_training)
      flow = conv_2(flow, is_training)
      return flow
  
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 2 for calling the ToyNet obj's underlying member method code with example usage: obj.layer_op(images, is_training) and returns: flow