import tensorflow as tf

           k_width,
           d_height=1,
           d_width=1,
           mode='SAME',
           input_layer=None,
           num_channels_in=None,
           batch_norm=None,
           activation='relu'):
    if input_layer is None:
      input_layer = self.top_layer
    if num_channels_in is None:
      num_channels_in = self.top_size
    name = 'conv' + str(self.counts['conv'])
    self.counts['conv'] += 1
    with tf.variable_scope(name):
      strides = [1, d_height, d_width, 1]
      if self.data_format == 'NCHW':
        strides = [strides[0], strides[3], strides[1], strides[2]]
      if mode != 'SAME_RESNET':
        conv = conv_layers.conv2d(
            input_layer,
            num_out_channels, [k_height, k_width],
            strides=[d_height, d_width],
            padding=mode,
            data_format=self.channel_pos,
            use_bias=False)
      else:  # Special padding mode for ResNet models
        if d_height == 1 and d_width == 1:
