code: """
   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 logging as log
  import math
  from typing import Dict
  
  import numpy as np
  
  from extensions.ops.Cast import Cast
  from extensions.ops.elementwise import Mul
  from extensions.ops.interpolate import Interpolate
  from mo.front.common.layout import get_height_dim, get_width_dim, get_depth_dim
  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, Node
  from mo.middle.replacement import MiddleReplacementPattern
  from mo.ops.shape import Shape
  from mo.ops.strided_slice import StridedSlice
  
  
  class UpsampleToResample(MiddleReplacementPattern):
      enabled = True
      force_clean_up = True
  
      def run_after(self):
          from extensions.middle.pass_separator import MiddleStart
          return [MiddleStart]
  
      def run_before(self):
          from extensions.middle.pass_separator import MiddleFinish
          return [MiddleFinish]
  
      def pattern(self):
          return dict(
              nodes=[
                  ('upsample', dict(kind='op', op='Upsample')),
                  ('output', dict(kind='data'))],
              edges=[('upsample', 'output')]
          )
  
      def replace_pattern(self, graph: Graph, match: Dict[str, Node]):
          log.debug('UpsampleToResample is triggered')
          upsample = match['upsample']
          upsample_name = upsample.soft_get('name', upsample.id)
          input_shape = upsample.in_port(0).data.get_shape()
          input_shape_rank = len(input_shape)
          if input_shape_rank not in [4, 5]:
              log.warning('The input shape is not 4D or 5D for op {}'.format(upsample.soft_get('name')))
              return
  
          depth_scale = None
          if len(upsample.in_nodes()) == 2:
              if upsample.in_node(1).value is None:
                  return
              scales = upsample.in_node(1).value
              assert len(scales) in (4, 5), 'Supported scales rank is 4 or 5, but it is {} for node {}'.format(
                  len(scales), upsample_name)
              if not (math.isclose(scales[0], 1, rel_tol=1e-5) and math.isclose(scales[1], 1, rel_tol=1e-5)):
                  return
              height_scale = scales[2]
              width_scale = scales[3]
              if len(scales) == 5:
                  depth_scale = scales[4]
          else:
              height_scale = upsample['height_scale']
              width_scale = upsample['width_scale']
  
          if 1 in upsample.in_ports() and not upsample.in_port(1).disconnected():
              upsample.in_port(1).disconnect()
  
          shape = Shape(graph, {'name': upsample_name + '/0_port'}).create_node()
  
          layout = graph.graph['layout']
  
          if input_shape_rank == 4:
              begin_value = int64_array([get_height_dim(layout, input_shape_rank)])
              factor_value = np.array([height_scale, width_scale])
          else:
              begin_value = int64_array([get_depth_dim(layout, input_shape_rank)])
              factor_value = np.array([depth_scale, height_scale, width_scale])
  
          ss = create_op_with_const_inputs(graph, StridedSlice,
                                           {1: begin_value,
                                            2: int64_array([get_width_dim(layout, input_shape_rank) + 1]),
                                            3: int64_array([1])
                                            },
                                           {'name': upsample_name + '/ss_0_port',
                                            'begin_mask': int64_array([1]),
                                            'end_mask': int64_array([1]),
                                            'new_axis_mask': int64_array([0]),
                                            'shrink_axis_mask': int64_array([0]),
                                            'ellipsis_mask': int64_array([0])
                                            }
                                           )
  
          mul = create_op_node_with_second_input(graph, Mul, factor_value, {'name': upsample_name + '/factor_mul_'})
  
          source = upsample.in_port(0).get_connection().get_source()
          source.connect(shape.in_port(0))
          shape.out_port(0).connect(ss.in_port(0))
  
          ss.out_port(0).connect(mul.in_port(0))
  
          # Create Interpolate operation
          if input_shape_rank == 4:
              axes = int64_array([get_height_dim(layout, input_shape_rank),
                                  get_width_dim(layout, input_shape_rank)])
          else:
              axes = int64_array([get_depth_dim(layout, input_shape_rank),
                                  get_height_dim(layout, input_shape_rank),
                                  get_width_dim(layout, input_shape_rank)])
  
          resample_op = Interpolate(graph, dict(name=upsample_name + '/Interpolate',
                                                axes=axes, mode=upsample.attrs()['mode'],
                                                antialias=0, convert_to_resample=True)).create_node()
  
          upsample.add_input_port(1, skip_if_exist=True)
          assert upsample.in_port(1).disconnected()
          mul.out_port(0).connect(resample_op.in_port(1))
  
          upsample.in_port(0).get_connection().set_destination(resample_op.in_port(0))
          upsample.out_port(0).get_connection().set_source(resample_op.out_port(0))
  
          convert_to_float = Cast(graph, dict(dst_type=np.float32)).create_node()
          convert_to_int = Cast(graph, dict(dst_type=np.int64)).create_node()
  
          mul.in_port(0).get_connection().insert_node(convert_to_float)
          mul.out_port(0).get_connection().insert_node(convert_to_int)
  
apis: numpy.array