import tensorflow as tf

              num_out_channels, [k_height, k_width],
              strides=[d_height, d_width],
              padding='VALID',
              data_format=self.channel_pos,
              use_bias=False)
      if batch_norm is None:
        batch_norm = self.use_batch_norm
      if not batch_norm:
        biases = tf.get_variable(
            'biases', [num_out_channels], self.data_type,
            tf.constant_initializer(0.0))
        biased = tf.reshape(
            tf.nn.bias_add(
                conv, biases, data_format=self.data_format),
            conv.get_shape())
      else:
        self.top_layer = conv
        self.top_size = num_out_channels
        biased = self.batch_norm(**self.batch_norm_config)
      if activation == 'relu':
