text: """
   Copyright (c) 2017-2019 Intel Corporation
  
   Licensed under the Apache License, Version 2.0 (the "License");
   you may not use this file except in compliance with the License.
   You may obtain a copy of the License at
  
        http://www.apache.org/licenses/LICENSE-2.0
  
   Unless required by applicable law or agreed to in writing, software
   distributed under the License is distributed on an "AS IS" BASIS,
   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   See the License for the specific language governing permissions and
   limitations under the License.
  """
  
  import numpy as np
  import networkx as nx
  from mo.ops.op import Op
  from mo.graph.graph import Graph
  from mo.back.replacement import BackReplacementPattern
  
  
  class CompatibilityL2NormalizationPattern(BackReplacementPattern):
  
      enabled = True
  
      def pattern(self):
          return dict(
              nodes=[
                  ('l2_normalization', dict(op='Normalize'))
              ],
              edges=[])
  
      def replace_pattern(self, graph: Graph, match: dict):
          """
          Adds Normalize layer weights, which are required by Inference Engine, 
          but do not always exist in MXNet model. 
          
          L2Normalization is mapped to Normalize layer
          so we need to generate Normalize weights filled with ones.
          
          Parameters
          ----------
          graph : Graph
             Graph with loaded model.
           match : dict
             Patterns which were found in graph structure.
          """
          l2_normalization_node = match['l2_normalization']
          if len(l2_normalization_node.in_nodes()) < 2:
              value = np.full([l2_normalization_node.in_node(0).shape[1]], 1.0, dtype=np.float32)
              weights_node = Op.create_input_data_node(graph, name=l2_normalization_node['name'] + '_weights', value=value)
              graph.create_edge(weights_node, l2_normalization_node, out_port=0, in_port=1, edge_attrs={'bin': 'weights'})
  