import tensorflow as tf


  def resnet_bottleneck_v1(self,
                           depth,
                           depth_bottleneck,
                           stride,
                           input_layer=None,
                           in_size=None):
    if input_layer is None:
      input_layer = self.top_layer
    if in_size is None:
      in_size = self.top_size
    name = 'resnet_v1' + str(self.counts['resnet_v1'])
    self.counts['resnet_v1'] += 1
    with tf.variable_scope(name):
      if depth == in_size:
        if stride == 1:
          shortcut = input_layer
        else:
          shortcut = self.mpool(
              1,
              1,
              stride,
              stride,
              input_layer=input_layer,
              num_channels_in=in_size)
      else:
