text: """
   Copyright (c) 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 logging as log
  
  import numpy as np
  
  from mo.back.replacement import BackReplacementPattern
  from mo.graph.graph import Graph, Node
  from mo.middle.pattern_match import for_each_sub_graph_recursively
  
  
  class TransposeFCWeights(BackReplacementPattern):
      """
      Repacks FC weights from IO (as in the TensorFlow) to OI (as in the Inference Engine).
      Marks input edge with the FullyConnected layer weights with 'bin' attribute.
      """
      enabled = True
      graph_condition = [
          lambda graph: graph.graph['fw'] in ['tf', 'mxnet'] and
                        not graph.graph['cmd_params'].generate_experimental_IR_V10
      ]
  
      def run_after(self):
          from extensions.back.RepackFCWeightsNHWCToNCHW import RepackFCWeightsNHWCToNCHW
          return [RepackFCWeightsNHWCToNCHW]
  
      def run_before(self):
          from extensions.back.ReshapeMutation import ReshapeMutation
          return [ReshapeMutation]
  
      def find_and_replace_pattern(self, graph: Graph):
          transposed_for_IE_flag = 'transposed_for_IE'
          for fc_node in graph.get_op_nodes(type='FullyConnected'):
              weights_node = fc_node.in_node(1)
              fc_node.in_edge(1)['bin'] = 'weights'
              if weights_node.has_and_set(transposed_for_IE_flag):
                  continue
              weights_node.value = np.transpose(weights_node.value)
              weights_node[transposed_for_IE_flag] = True
              log.debug("Transposed weights {} for FC node {}; weights.shape = {}"
                        "".format(weights_node.name, fc_node.name, weights_node.shape))
              weights_node.shape = np.array(weights_node.value.shape)
  
          # FIXME remove this line and make transformation run recursively when recursive transformation run feature is
          # implemented
          for_each_sub_graph_recursively(graph, self.find_and_replace_pattern)
  