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.front.caffe.extractors.utils import embed_input
  from mo.front.common.partial_infer.elemental import copy_shape_infer
  
  
  def batch_norm_ext(pb_layer, pb_model):
      """
      Extracts properties of the BatchNorm layer.
      In case of scale, scale is merged into mean and variance
      Args:
          pb_layer: proto layer, contains own properties of the layer, i.e epsilon
          pb_model: caffemodel layer, contains blobs with 0: mean, 1: variance, (opt)2: scale
  
      Returns:
          attrs object with type, partial inference function and mean/variance properties.
      """
      assert pb_layer, 'Protobuf layer can not be empty'
      param = pb_layer.batch_norm_param
      attrs = {
          'op': 'BatchNormalization',
          'type': 'BatchNormalization',
          'eps': param.eps,
          'infer': copy_shape_infer
      }
  
      if not pb_model:
          return attrs
  
      blobs = pb_model.blobs
      assert len(blobs) >= 2, 'BatchNorm accepts not less then two input blobs'
      mean = np.array(blobs[0].data)
      variance = np.array(blobs[1].data)
  
      if len(blobs) == 3:
          scale = blobs[2].data[0]
          if scale != 0:
              scale = 1.0 / scale
          mean *= scale
          variance *= scale
  
      embed_input(attrs, 1, 'gamma', np.ones(mean.shape), 'gamma')
      embed_input(attrs, 2, 'beta', np.zeros(variance.shape), 'beta')
      embed_input(attrs, 3, 'mean', mean, 'biases')
      embed_input(attrs, 4, 'variance', variance, 'weights')
  
      return attrs
  