from tensorflow.python.layers import convolutional as conv_layers

            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:
          conv = conv_layers.conv2d(
              input_layer,
              num_out_channels, [k_height, k_width],
              strides=[d_height, d_width],
              padding='SAME',
              data_format=self.channel_pos,
              use_bias=False)
        else:
