text: """
   Copyright (C) 2018-2021 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 extensions.back.ReshapeMutation import ReshapeMutation
  from extensions.back.StridedSliceMasksNormalizer import StridedSliceMasksNormalizer
  from mo.back.replacement import BackReplacementPattern
  from mo.front.common.partial_infer.utils import int64_array
  from mo.front.tf.graph_utils import create_op_with_const_inputs, create_op_node_with_second_input
  from mo.graph.graph import Graph
  from mo.ops.reshape import Reshape
  from mo.ops.strided_slice import StridedSlice
  
  
  class ProposalMutation(BackReplacementPattern):
      enabled = True
      force_clean_up = True
  
      def run_before(self):
          return [ReshapeMutation, StridedSliceMasksNormalizer]
  
      @staticmethod
      def pattern():
          return dict(
              nodes=[('proposal', {'type': 'Proposal'})],
              edges=[],
          )
  
      @staticmethod
      def replace_pattern(graph: Graph, match: dict):
          node = match['proposal']
          assert len(node.in_ports()) == 3, "Proposal op must have exactly 3 input ports"
          im_info_shape = node.in_port(2).data.get_shape()
          assert im_info_shape is not None
  
          if np.array_equal(im_info_shape, [1, 6]):
              log.error('The model contains Proposal layer "{}" with input of shape [1, 6]. Inference Engine '
                        'implementation of the Proposal layer uses only 4 first values (indices 0, 1, 2 and 3). '
                        'Elements with indices 4 and 5 will be ignored.'.format(node.soft_get('name', node.id)),
                        extra={'is_warning': True})
  
              cropped_im_info = create_op_with_const_inputs(graph, StridedSlice, {1: np.array([0, 0], dtype=np.int32),
                                                                                  2: np.array([1, 3], dtype=np.int32),
                                                                                  3: np.array([1, 1], dtype=np.int32)},
                                                            {'name': 'cropped_im_info',
                                                             'begin_mask': int64_array([1, 1]),
                                                             'end_mask': int64_array([1, 1]),
                                                             'new_axis_mask': int64_array([0, 0]),
                                                             'shrink_axis_mask': int64_array([0, 0]),
                                                             'ellipsis_mask': int64_array([0, 0]),
                                                             'override_output_shape': True,
                                                             })
  
              node.in_port(2).get_connection().insert_node(cropped_im_info)
  
              # update the im_info_shape so the next 'if' statement become true
              im_info_shape = int64_array([1, 3])
  
          if np.array_equal(im_info_shape, [1, 3]) or np.array_equal(im_info_shape, [1, 4]):
              reshape = create_op_node_with_second_input(graph, Reshape, [im_info_shape[1]], {'name': 'im_info/Reshape'})
              node.in_port(2).get_connection().set_destination(reshape.in_port(0))
              reshape.out_port(0).connect(node.in_port(2))
  