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.
  """
  
  from mo.front.tf.extractors.concat import tf_concat_ext
  from mo.front.tf.extractors.fused_bn import tf_fused_bn_extractor
  from mo.front.tf.extractors.native_tf import native_tf_node_extractor
  from mo.front.tf.extractors.pack import tf_pack_ext
  from mo.front.tf.extractors.random_uniform import tf_random_uniform_ext
  from mo.front.tf.extractors.space_to_batch import tf_space_to_batch_ext, tf_batch_to_space_ext
  from mo.front.tf.extractors.utils import get_tf_node_port
  from mo.graph.graph import Node
  
  
  def get_tf_edges(node: Node):
      """
      By TF/NX node find all inputs and return list of all edges.
      Edge direction represents data flow (from source op to this node).
      So the resulting list contains all input edges for a given node.
      Edge attributes: 'in' is index of input port for a given node, 'out' is an index
      of output port of some other node that produces input data for this node.
      """
      edge_list = []
      for in_port, src_node_id in enumerate(node.pb.input):
          src_node, src_port = get_tf_node_port(src_node_id)
          cf_flag = False
          if src_node[0] == '^':
              src_node = src_node[1:]
              cf_flag = True
          edge = (src_node, node.id, {
              'in': in_port,
              'out': src_port,
              'fw_tensor_debug_info': [(src_node_id, src_port)],  # debug anchor for a framework tensor name and port
              'in_attrs': ['in', 'control_flow_edge', 'permutation'],
              'out_attrs': ['out', 'permutation'],
              'data_attrs': ['fw_tensor_debug_info'],
              'control_flow_edge': cf_flag
          })
          edge_list.append(edge)
      return edge_list
  
  
  def node_pb_arg(pb_extractor: callable):
      return lambda node: pb_extractor(node.pb)
  
  
  tf_op_extractors = {
      'TFCustomSubgraphCall': node_pb_arg(lambda pb: None),
      'FusedBatchNorm': node_pb_arg(tf_fused_bn_extractor),
      'FusedBatchNormV2': node_pb_arg(tf_fused_bn_extractor),
      'FusedBatchNormV3': node_pb_arg(tf_fused_bn_extractor),
      'ConcatV2': node_pb_arg(tf_concat_ext),
      'Pack': node_pb_arg(tf_pack_ext),
      'RandomUniform': node_pb_arg(tf_random_uniform_ext),
      'SpaceToBatchND': node_pb_arg(tf_space_to_batch_ext),
      'BatchToSpaceND': node_pb_arg(tf_batch_to_space_ext),
  }
  
  
  def common_tf_fields(node: Node):
      return {
          'kind': 'op',
          'name': node.pb.name,
          'op': node.pb.op,
      }
  
  
  def tf_op_extractor(node: Node, lowered_keys_map: dict):
      # all required attributes for the 'TFCustomSubgraphCall' are set during their initialization
      if (node.has('op') and node.op == 'TFCustomSubgraphCall') or (not node.has_valid('pb')):
          return True, node.graph.node[node.id]
  
      result = common_tf_fields(node)
      node.graph.node[node.id].update(result)
      supported = False
      op = result['op'].lower()
      if op in lowered_keys_map:
          op = lowered_keys_map[op]
          assert op in tf_op_extractors
          attrs = tf_op_extractors[op](node)
          if attrs:
              result.update(attrs)
              supported = True
      new_attrs = native_tf_node_extractor(node.pb)
      new_attrs.update(result)
      result = new_attrs
      return supported, result
  