code: def add_l2_normalize(self, name, input_name, output_name, epsilon = 1e-5):
          
  
          spec = self.spec
          nn_spec = self.nn_spec
  
          # Add a new layer
          spec_layer = nn_spec.layers.add()
          spec_layer.name = name
          spec_layer.input.append(input_name)
          spec_layer.output.append(output_name)
  
          spec_layer_params = spec_layer.l2normalize
          spec_layer_params.epsilon = epsilon
docs: Add L2 normalize layer. Normalizes the input by the L2 norm, i.e. divides by the
          the square root of the sum of squares of all elements of the input along C, H and W dimensions.
  
          Parameters
          ----------
          name: str
              The name of this layer.
  
          input_name: str
              The input blob name of this layer.
          output_name: str
              The output blob name of this layer.
  
          epsilon: float
              small bias to avoid division by zero.
  
  
          See Also
          --------
          add_mvn, add_lrn