code: def _conv(self, name, x, filter_size, in_filters, out_filters, strides):
          
          with tf.variable_scope(name):
              n = filter_size * filter_size * out_filters
              kernel = tf.get_variable(
                  "DW", [filter_size, filter_size, in_filters, out_filters],
                  tf.float32,
                  initializer=tf.random_normal_initializer(
                      stddev=np.sqrt(2.0 / n)))
              return tf.nn.conv2d(x, kernel, strides, padding="SAME")
docs: Convolution.