text: """
   Copyright (c) 2018-2019 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 numpy as np
  
  from mo.graph.graph import Node, Graph
  from mo.graph.perm_inputs import PermuteInputs
  from mo.ops.op import Op
  
  
  class Broadcast(Op):
      """ Broadcast tensor to a given shape with optional axis parameter
  
          Inputs:
              [0] - tensor to be broadcasted
              [1] - shape to be broadcast to
              [2] - optional axis paramater that which axis are allowed to be broadcasted
      """
  
      op = 'Broadcast'
      enabled = True
  
      def __init__(self, graph: Graph, attrs: dict):
          super().__init__(graph, {
              'kind': 'op',
              'type': __class__.op,
              'op': __class__.op,
              'in_ports_count': 3,
              'out_ports_count': 1,
              'force_precision_in_ports': {1: 'int64' if graph.graph['cmd_params'].generate_experimental_IR_V10 else 'int32',
                                           2: 'int64' if graph.graph['cmd_params'].generate_experimental_IR_V10 else 'int32',
                                           },
              'infer': __class__.infer,
          }, attrs)
  
      @staticmethod
      def infer(node: Node):
          # TODO Add necessary checks and asserts
          b_value = node.in_port(0).data.get_value()
          b_shape = node.in_port(1).data.get_value()
          assert b_shape is not None
          node.out_port(0).data.set_shape(b_shape)
  
          PermuteInputs().set_input_permutation(node.in_node(1), node, 'output:0', 'shape')
          if b_value is not None:
              new_value = np.broadcast_to(b_value, b_shape)
              node.out_port(0).data.set_value(new_value)
  
  