text: """
   Copyright (C) 2018-2020 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 unittest
  
  import numpy as np
  from generator import generator, generate
  
  from extensions.back.ReduceToPooling import ReduceReplacer, ReduceMerge
  from mo.front.common.partial_infer.utils import int64_array
  from mo.middle.passes.eliminate import shape_inference
  from mo.middle.passes.eliminate_test import build_graph
  from mo.utils.ir_engine.compare_graphs import compare_graphs
  
  # The dictionary with nodes attributes used to build various graphs. A key is the name of the node and the value is the
  # dictionary with node attributes.
  nodes_attributes = {
      # Placeholder layers
      'placeholder_1': {'shape': None, 'type': 'Parameter', 'kind': 'op', 'op': 'Parameter'},
      'placeholder_1_data': {'value': None, 'shape': None, 'kind': 'data', 'data_type': None},
  
      # Reduce layers
      'const': {'type': 'Const', 'value': None, 'kind': 'op'},
      'const_data': {'kind': 'data', 'value': None, 'shape': None},
      'reduce_1': {'type': 'Reduce', 'kind': 'op', 'op': 'Reduce'},
      'reduce_1_data': {'value': None, 'shape': None, 'kind': 'data'},
      'const_2': {'type': 'Const', 'value': None, 'kind': 'op'},
      'const_2_data': {'kind': 'data', 'value': None, 'shape': None},
      'reduce_2': {'type': 'Reduce', 'kind': 'op', 'op': 'Reduce'},
      'reduce_2_data': {'value': None, 'shape': None, 'kind': 'data'},
  
      # Reshape layers
      'reshape_1': {'type': 'Reshape', 'kind': 'op', 'op': 'Reshape'},
      'reshape_1_data': {'value': None, 'shape': None, 'kind': 'data'},
      'reshape_1_const': {'type': 'Const', 'kind': 'op', 'op': 'Const', 'value': None},
      'reshape_1_const_data': {'kind': 'data', 'value': None, 'shape': None},
  
      'reshape_2': {'type': 'Reshape', 'kind': 'op', 'op': 'Reshape'},
      'reshape_2_data': {'value': None, 'shape': None, 'kind': 'data'},
      'reshape_2_const': {'type': 'Const', 'kind': 'op', 'op': 'Const', 'value': None},
      'reshape_2_const_data': {'kind': 'data', 'value': None, 'shape': None},
  
      # Pooling
      'pooling': {'type': 'Pooling', 'kind': 'op', 'op': 'Pooling'},
      'pooling_data': {'value': None, 'shape': None, 'kind': 'data'},
  
      # Power
      'power': {'type': 'Power', 'kind': 'op', 'op': 'AttributedPower'},
      'power_data': {'value': None, 'shape': None, 'kind': 'data'},
  
      # Concat
      'concat': {'type': 'Concat', 'kind': 'op', 'op': 'Concat'},
  
      # Result
      'result': {'type': 'Result', 'kind': 'op', 'op': 'Result'},
  }
  
  
  class ReduceReplacerTest(unittest.TestCase):
      def test1(self):
          #   Original graph
          #   data(1,64,1)-->Reduce(axis=1,keep_dims=True)-->data(1,1,1)
          #
          #   Reference graph
          #   data(1,64,1)->Reshape(1,1,8,8)->Pool(1,1,1,1)->Reshape(1,1,1)
          #
          graph = build_graph(nodes_attributes,
                              [('placeholder_1', 'placeholder_1_data'),
                               ('placeholder_1_data', 'reduce_1'),
                               ('const', 'const_data'),
                               ('const_data', 'reduce_1', {'in': 1}),
                               ('reduce_1', 'reduce_1_data'),
                               ('reduce_1_data', 'concat'),
                               ],
                              {'placeholder_1_data': {'shape': int64_array([1, 64, 1])},
                               'reduce_1': {'keep_dims': True, 'type': 'ReduceMean'},
                               'const_data': {'value': int64_array([1])},
                               'reduce_1_data': {'shape': int64_array([1, 1, 1])},
                               }, nodes_with_edges_only=True)
  
          graph.graph['layout'] = 'NCHW'
  
          graph_ref = build_graph(nodes_attributes,
                                  [('placeholder_1', 'placeholder_1_data'),
                                   ('placeholder_1_data', 'reshape_1'),
                                   ('reshape_1_const', 'reshape_1_const_data'),
                                   ('reshape_1_const_data', 'reshape_1'),
                                   ('reshape_1', 'reshape_1_data'),
                                   ('reshape_1_data', 'pooling'),
                                   ('pooling', 'pooling_data'),
                                   ('pooling_data', 'reshape_2'),
                                   ('reshape_2_const', 'reshape_2_const_data'),
                                   ('reshape_2_const_data', 'reshape_2'),
                                   ('reshape_2', 'reshape_2_data'),
                                   ('reshape_2_data', 'concat'),
                                   ],
                                  {'placeholder_1_data': {'shape': int64_array([1, 64, 1])},
                                   'reshape_1_const': {'value': int64_array([0, 1, 8, 8]), 'shape': int64_array([4])},
                                   'reshape_1_const_data': {'value': int64_array([0, 1, 8, 8]),
                                                            'shape': int64_array([4])},
                                   'reshape_1_data': {'shape': int64_array([1, 1, 8, 8])},
                                   'pooling': {'window': int64_array([1, 1, 8, 8])},
                                   'pooling_data': {'shape': int64_array([1, 1, 1, 1])},
                                   'reshape_2_const': {'value': int64_array([0, 1, 1]), 'shape': int64_array([3])},
                                   'reshape_2_const_data': {'value': int64_array([0, 1, 1]), 'shape': int64_array([3])},
                                   'reshape_2_data': {'shape': int64_array([1, 1, 1])},
                                   }, nodes_with_edges_only=True)
  
          ReduceReplacer().find_and_replace_pattern(graph)
          shape_inference(graph)
  
          (flag, resp) = compare_graphs(graph, graph_ref, 'concat', check_op_attrs=True)
          self.assertTrue(flag, resp)
  
      def test2(self):
          #   Original graph
          #   data(1,3,64,64)-->Reduce(axis=2,keep_dims=True)-->data(1,3,1,64)
          #
          #   Reference graph
          #   data(1,3,64,64)->Pool(1,3,1,64)->Reshape(1,3,1,64)
          #
          graph = build_graph(nodes_attributes,
                              [('placeholder_1', 'placeholder_1_data'),
                               ('placeholder_1_data', 'reduce_1'),
                               ('const', 'const_data'),
                               ('const_data', 'reduce_1', {'in': 1}),
                               ('reduce_1', 'reduce_1_data'),
                               ('reduce_1_data', 'concat'),
                               ],
                              {'placeholder_1': {'shape': int64_array([1, 3, 64, 64])},
                               'placeholder_1_data': {'shape': int64_array([1, 3, 64, 64])},
                               'reduce_1': {'keep_dims': True, 'type': 'ReduceMean'},
                               'const_data': {'value': int64_array([2])},
                               'reduce_1_data': {'shape': int64_array([1, 3, 1, 64])},
                               }, nodes_with_edges_only=True)
  
          graph.graph['layout'] = 'NCHW'
  
          graph_ref = build_graph(nodes_attributes,
                                  [('placeholder_1', 'placeholder_1_data'),
                                   ('placeholder_1_data', 'pooling'),
                                   ('pooling', 'pooling_data'),
                                   ('pooling_data', 'reshape_2'),
                                   ('reshape_2_const', 'reshape_2_const_data'),
                                   ('reshape_2_const_data', 'reshape_2'),
                                   ('reshape_2', 'reshape_2_data'),
                                   ('reshape_2_data', 'concat'),
                                   ],
                                  {'placeholder_1': {'shape': int64_array([1, 3, 64, 64])},
                                   'placeholder_1_data': {'shape': int64_array([1, 3, 64, 64])},
                                   'pooling': {'window': int64_array([1, 1, 64, 1])},
                                   'pooling_data': {'shape': int64_array([1, 3, 1, 64])},
                                   'reshape_2_const': {'value': int64_array([0, 3, 1, 64]), 'shape': int64_array([4])},
                                   'reshape_2_const_data': {'value': int64_array([0, 3, 1, 64]),
                                                            'shape': int64_array([4])},
                                   'reshape_2_data': {'shape': int64_array([1, 3, 1, 64])},
                                   }, nodes_with_edges_only=True)
  
          ReduceReplacer().find_and_replace_pattern(graph)
          shape_inference(graph)
  
          (flag, resp) = compare_graphs(graph, graph_ref, 'concat', check_op_attrs=True)
          self.assertTrue(flag, resp)
  
      def test3(self):
          #   Original graph
          #   data(1,3,64,64)-->Reduce(axis=[2,3],keep_dims=True)-->data(1,3,1,1)
          #
          #   Reference graph
          #   data(1,3,64,64)->Pool(1,3,1,1)->Reshape(1,3,1,1)
          #
          graph = build_graph(nodes_attributes,
                              [('placeholder_1', 'placeholder_1_data'),
                               ('placeholder_1_data', 'reduce_1'),
                               ('const', 'const_data'),
                               ('const_data', 'reduce_1', {'in': 1}),
                               ('reduce_1', 'reduce_1_data'),
                               ('reduce_1_data', 'concat'),
                               ],
                              {'placeholder_1': {'shape': int64_array([1, 3, 64, 64])},
                               'placeholder_1_data': {'shape': int64_array([1, 3, 64, 64])},
                               'reduce_1': {'keep_dims': True, 'type': 'ReduceMean'},
                               'const_data': {'value': int64_array([2, 3])},
                               'reduce_1_data': {'shape': int64_array([1, 3, 1, 1])},
                               }, nodes_with_edges_only=True)
  
          graph.graph['layout'] = 'NCHW'
  
          graph_ref = build_graph(nodes_attributes,
                                  [('placeholder_1', 'placeholder_1_data'),
                                   ('placeholder_1_data', 'pooling'),
                                   ('pooling', 'pooling_data'),
                                   ('pooling_data', 'reshape_2'),
                                   ('reshape_2_const', 'reshape_2_const_data'),
                                   ('reshape_2_const_data', 'reshape_2'),
                                   ('reshape_2', 'reshape_2_data'),
                                   ('reshape_2_data', 'concat'),
                                   ],
                                  {'placeholder_1': {'shape': int64_array([1, 3, 64, 64])},
                                   'placeholder_1_data': {'shape': int64_array([1, 3, 64, 64])},
                                   'pooling': {'window': int64_array([1, 1, 64, 64])},
                                   'pooling_data': {'shape': int64_array([1, 3, 1, 1])},
                                   'reshape_2_const': {'value': int64_array([0, 3, 1, 1]), 'shape': int64_array([4])},
                                   'reshape_2_const_data': {'value': int64_array([0, 3, 1, 1]),
                                                            'shape': int64_array([4])},
                                   'reshape_2_data': {'shape': int64_array([1, 3, 1, 1])},
                                   }, nodes_with_edges_only=True)
  
          ReduceReplacer().find_and_replace_pattern(graph)
          shape_inference(graph)
  
          (flag, resp) = compare_graphs(graph, graph_ref, 'concat', check_op_attrs=True)
          self.assertTrue(flag, resp)
  
      def test4(self):
          #   Original graph
          #   data(2,3,64,64)-->Reduce(axis=[1,2,3],keep_dims=False)-->data(2)
          #
          #   Reference graph
          #   data(2,3,64,64)->Reshape(2,1,96,128)->Pool(2,1,1,1)->Reshape(2)
          #
          graph = build_graph(nodes_attributes,
                              [('placeholder_1', 'placeholder_1_data'),
                               ('placeholder_1_data', 'reduce_1'),
                               ('const', 'const_data'),
                               ('const_data', 'reduce_1', {'in': 1}),
                               ('reduce_1', 'reduce_1_data'),
                               ('reduce_1_data', 'concat'),
                               ],
                              {'placeholder_1': {'shape': int64_array([2, 3, 64, 64])},
                               'placeholder_1_data': {'shape': int64_array([2, 3, 64, 64])},
                               'reduce_1': {'keep_dims': False, 'type': 'ReduceMean'},
                               'const_data': {'value': int64_array([1, 2, 3])},
                               'reduce_1_data': {'shape': int64_array([2])},
                               }, nodes_with_edges_only=True)
  
          graph.graph['layout'] = 'NCHW'
  
          graph_ref = build_graph(nodes_attributes,
                                  [('placeholder_1', 'placeholder_1_data'),
                                   ('placeholder_1_data', 'reshape_1'),
                                   ('reshape_1_const', 'reshape_1_const_data'),
                                   ('reshape_1_const_data', 'reshape_1'),
                                   ('reshape_1', 'reshape_1_data'),
                                   ('reshape_1_data', 'pooling'),
                                   ('pooling', 'pooling_data'),
                                   ('pooling_data', 'reshape_2'),
                                   ('reshape_2_const', 'reshape_2_const_data'),
                                   ('reshape_2_const_data', 'reshape_2'),
                                   ('reshape_2', 'reshape_2_data'),
                                   ('reshape_2_data', 'concat'),
                                   ],
                                  {'placeholder_1': {'shape': int64_array([2, 3, 64, 64])},
                                   'placeholder_1_data': {'shape': int64_array([2, 3, 64, 64])},
                                   'reshape_1_const': {'value': int64_array([0, 1, 96, 128]),
                                                       'shape': int64_array([4])},
                                   'reshape_1_const_data': {'value': int64_array([0, 1, 96, 128]),
                                                            'shape': int64_array([4])},
                                   'reshape_1_data': {'shape': int64_array([2, 1, 96, 128])},
                                   'pooling': {'window': int64_array([1, 1, 96, 128])},
                                   'pooling_data': {'shape': int64_array([2, 1, 1, 1])},
                                   'reshape_2_const': {'value': int64_array([0]), 'shape': int64_array([1])},
                                   'reshape_2_const_data': {'value': int64_array([0]), 'shape': int64_array([1])},
                                   'reshape_2_data': {'shape': int64_array([2])},
                                   }, nodes_with_edges_only=True)
  
          ReduceReplacer().find_and_replace_pattern(graph)
          shape_inference(graph)
  
          (flag, resp) = compare_graphs(graph, graph_ref, 'concat', check_op_attrs=True)
          self.assertTrue(flag, resp)
  
      def test5(self):
          #   Original graph
          #   data(1, 16, 64, 64, 64, 4)-->Reduce(axis=[5],keep_dims=False)-->data(1, 16, 64, 64, 64)
          #
          #   Reference graph
          #   data(1, 16, 64, 64, 64, 4)->Reshape(1*16*64*64, 64, 2, 2)->Pool(1, 1, 2, 2)->Reshape(1, 16, 64, 64, 64)
          #
          graph = build_graph(nodes_attributes,
                              [('placeholder_1', 'placeholder_1_data'),
                               ('placeholder_1_data', 'reduce_1'),
                               ('const', 'const_data'),
                               ('const_data', 'reduce_1', {'in': 1}),
                               ('reduce_1', 'reduce_1_data'),
                               ('reduce_1_data', 'concat'),
                               ],
                              {'placeholder_1': {'shape': int64_array([1, 16, 64, 64, 64, 4])},
                               'placeholder_1_data': {'shape': int64_array([1, 16, 64, 64, 64, 4])},
                               'reduce_1': {'keep_dims': False, 'type': 'ReduceMax'},
                               'const_data': {'value': int64_array([5])},
                               'reduce_1_data': {'shape': int64_array([1, 16, 64, 64, 64])},
                               }, nodes_with_edges_only=True)
  
          graph.graph['layout'] = 'NCHW'
  
          graph_ref = build_graph(nodes_attributes,
                                  [('placeholder_1', 'placeholder_1_data'),
                                   ('placeholder_1_data', 'reshape_1'),
                                   ('reshape_1_const', 'reshape_1_const_data'),
                                   ('reshape_1_const_data', 'reshape_1'),
                                   ('reshape_1', 'reshape_1_data'),
                                   ('reshape_1_data', 'pooling'),
                                   ('pooling', 'pooling_data'),
                                   ('pooling_data', 'reshape_2'),
                                   ('reshape_2_const', 'reshape_2_const_data'),
                                   ('reshape_2_const_data', 'reshape_2'),
                                   ('reshape_2', 'reshape_2_data'),
                                   ('reshape_2_data', 'concat'),
                                   ],
                                  {'placeholder_1': {'shape': int64_array([1, 16, 64, 64, 64, 4])},
                                   'placeholder_1_data': {'shape': int64_array([1, 16, 64, 64, 64, 4])},
                                   'reshape_1_const': {'value': int64_array([0, 4194304, 2, 2]),
                                                       'shape': int64_array([4])},
                                   'reshape_1_const_data': {'value': int64_array([0, 4194304, 2, 2]),
                                                            'shape': int64_array([4])},
                                   'reshape_1_data': {'shape': int64_array([1, 4194304, 2, 2])},
                                   'pooling': {'window': int64_array([1, 1, 2, 2])},
                                   'pooling_data': {'shape': int64_array([1, 4194304, 1, 1])},
                                   'reshape_2_const': {'value': int64_array([0, 16, 64, 64, 64]),
                                                       'shape': int64_array([5])},
                                   'reshape_2_const_data': {'value': int64_array([0, 16, 64, 64, 64]),
                                                            'shape': int64_array([5])},
                                   'reshape_2_data': {'shape': int64_array([1, 16, 64, 64, 64])},
                                   }, nodes_with_edges_only=True)
  
          ReduceReplacer().find_and_replace_pattern(graph)
          shape_inference(graph)
  
          (flag, resp) = compare_graphs(graph, graph_ref, 'concat', check_op_attrs=True)
          self.assertTrue(flag, resp)
  
      def test6(self):
          #   Original graph
          #   data(1,64,1)-->Reduce(axis=-2,keep_dims=True, reduce_type=Sum)-->data(1,1,1)
          #
          #   Reference graph
          #   data(1,61,1)->Reshape(1,1,8,8)->Pool(1,1,1,1)->Reshape(1,1,1)->Power(scale=64)
          #
          graph = build_graph(nodes_attributes,
                              [('placeholder_1', 'placeholder_1_data'),
                               ('placeholder_1_data', 'reduce_1'),
                               ('const', 'const_data'),
                               ('const_data', 'reduce_1', {'in': 1}),
                               ('reduce_1', 'reduce_1_data'),
                               ('reduce_1_data', 'concat'),
                               ],
                              {'placeholder_1': {'shape': int64_array([1, 64, 1])},
                               'placeholder_1_data': {'shape': int64_array([1, 64, 1])},
                               'reduce_1': {'keep_dims': True, 'type': 'ReduceSum'},
                               'const_data': {'value': int64_array([-2])},
                               'reduce_1_data': {'shape': int64_array([1, 1, 1])},
                               }, nodes_with_edges_only=True)
  
          graph.graph['layout'] = 'NCHW'
  
          graph_ref = build_graph(nodes_attributes,
                                  [('placeholder_1', 'placeholder_1_data'),
                                   ('placeholder_1_data', 'reshape_1'),
                                   ('reshape_1_const', 'reshape_1_const_data'),
                                   ('reshape_1_const_data', 'reshape_1'),
                                   ('reshape_1', 'reshape_1_data'),
                                   ('reshape_1_data', 'pooling'),
                                   ('pooling', 'pooling_data'),
                                   ('pooling_data', 'reshape_2'),
                                   ('reshape_2_const', 'reshape_2_const_data'),
                                   ('reshape_2_const_data', 'reshape_2'),
                                   ('reshape_2', 'reshape_2_data'),
                                   ('reshape_2_data', 'power'),
                                   ('power', 'power_data'),
                                   ('power_data', 'concat'),
                                   ],
                                  {'placeholder_1': {'shape': int64_array([1, 64, 1])},
                                   'placeholder_1_data': {'shape': int64_array([1, 64, 1])},
                                   'reshape_1_const': {'value': int64_array([0, 1, 8, 8]), 'shape': int64_array([4])},
                                   'reshape_1_const_data': {'value': int64_array([0, 1, 8, 8]),
                                                            'shape': int64_array([4])},
                                   'reshape_1_data': {'shape': int64_array([1, 1, 8, 8])},
                                   'pooling': {'window': int64_array([1, 1, 8, 8])},
                                   'pooling_data': {'shape': int64_array([1, 1, 1, 1])},
                                   'reshape_2_const': {'value': int64_array([0, 1, 1]), 'shape': int64_array([3])},
                                   'reshape_2_const_data': {'value': int64_array([0, 1, 1]), 'shape': int64_array([3])},
                                   'reshape_2_data': {'shape': int64_array([1, 1, 1])},
                                   'power': {'scale': 64.0},
                                   'power_data': {'shape': int64_array([1, 1, 1])},
                                   }, nodes_with_edges_only=True)
  
          ReduceReplacer().find_and_replace_pattern(graph)
          shape_inference(graph)
  
          (flag, resp) = compare_graphs(graph, graph_ref, 'concat', check_op_attrs=True)
          self.assertTrue(flag, resp)
  
      def test7(self):
          #   Original graph
          #   data(1,1,64,64)-->Reduce(axis=-1,keep_dims=True, reduce_type=Mean)-->Reduce(same, axis=-2)-->data(1,1,1,1)
          #
          #   Reference graph
          #   data(1,61,1)->Reshape(1,1,8,8)->Pool(1,1,1,1)->Reshape(1,1,1)->Power(scale=64)
          #
          graph = build_graph(nodes_attributes,
                              [('placeholder_1', 'placeholder_1_data'),
                               ('placeholder_1_data', 'reduce_1'),
                               ('const', 'const_data'),
                               ('const_data', 'reduce_1', {'in': 1}),
                               ('reduce_1', 'reduce_1_data'),
                               ('reduce_1_data', 'reduce_2'),
                               ('const_2', 'const_2_data'),
                               ('const_2_data', 'reduce_2', {'in': 1}),
                               ('reduce_2', 'reduce_2_data'),
                               ('reduce_2_data', 'result'),
                               ],
                              {'placeholder_1': {'shape': int64_array([1, 1, 64, 64])},
                               'placeholder_1_data': {'shape': int64_array([1, 1, 64, 64])},
                               'reduce_1': {'keep_dims': True, 'type': 'ReduceMean'},
                               'const_data': {'value': int64_array([-1])},
                               'reduce_1_data': {'shape': int64_array([1, 1, 64, 1])},
                               'reduce_2': {'keep_dims': True, 'type': 'ReduceMean'},
                               'const_2_data': {'value': int64_array([-2])},
                               'reduce_2_data': {'shape': int64_array([1, 1, 1, 1])},
                               }, nodes_with_edges_only=True)
  
          graph.graph['layout'] = 'NCHW'
  
          graph_ref = build_graph(nodes_attributes,
                                  [('placeholder_1', 'placeholder_1_data'),
                                   ('placeholder_1_data', 'reshape_1'),
                                   ('reshape_1_const', 'reshape_1_const_data'),
                                   ('reshape_1_const_data', 'reshape_1'),
                                   ('reshape_1', 'reshape_1_data'),
                                   ('reshape_1_data', 'pooling'),
                                   ('pooling', 'pooling_data'),
                                   ('pooling_data', 'reshape_2'),
                                   ('reshape_2_const', 'reshape_2_const_data'),
                                   ('reshape_2_const_data', 'reshape_2'),
                                   ('reshape_2', 'reshape_2_data'),
                                   ('reshape_2_data', 'result'),
                                   ],
                                  {'placeholder_1': {'shape': int64_array([1, 1, 64, 64])},
                                   'reshape_1_const': {'value': int64_array([0, 1, 8, 8]), 'shape': int64_array([4])},
                                   'reshape_1_const_data': {'value': int64_array([0, 1, 8, 8]),
                                                            'shape': int64_array([4])},
                                   'reshape_1_data': {'shape': int64_array([1, 1, 8, 8])},
                                   'pooling': {'window': int64_array([1, 1, 8, 8])},
                                   'pooling_data': {'shape': int64_array([1, 1, 1, 1])},
                                   'reshape_2_const': {'value': int64_array([0, 1, 1, 1]), 'shape': int64_array([4])},
                                   'reshape_2_const_data': {'value': int64_array([0, 1, 1, 1]),
                                                            'shape': int64_array([4])},
                                   'reshape_2_data': {'shape': int64_array([1, 1, 1, 1])},
                                   }, nodes_with_edges_only=True)
  
          ReduceMerge().find_and_replace_pattern(graph)
          ReduceReplacer().find_and_replace_pattern(graph)
  
          (flag, resp) = compare_graphs(graph, graph_ref, 'result', check_op_attrs=True)
          self.assertTrue(flag, resp)
  
  
  @generator
  class DimNormalizer(unittest.TestCase):
      @generate(*[
          (1, [1, 1]),
          (2, [1, 2]),
          (3, [1, 3]),
          (4, [2, 2]),
          (5, [1, 5]),
          (9, [3, 3]),
          (19, [1, 19]),
          (1000, [25, 40]),
          (1000003, [1, 1000003]),
          (1005973, [997, 1009]),
      ])
      def test_initial_reshape_dim_normalizer(self, number, expected_output):
          window = ReduceReplacer.initial_reshape_dim_normalizer(number)
          self.assertIsNotNone(window, "window is None for i={}".format(number))
          self.assertEqual(number, np.prod(window), "{} != prod({})".format(number, window))
          self.assertEqual(expected_output, window, "{} != {}".format(expected_output, window))
  