code: def visualize_tensors(name, imgs, scale_func=lambda x: (x + 1.) * 128., max_outputs=1):
      
      xy = scale_func(tf.concat(imgs, axis=2))
      xy = tf.cast(tf.clip_by_value(xy, 0, 255), tf.uint8, name='viz')
      tf.summary.image(name, xy, max_outputs=30)
docs: Generate tensor for TensorBoard (casting, clipping)
  
      Args:
          name: name for visualization operation
          *imgs: multiple tensors as list
          scale_func: scale input tensors to fit range [0, 255]
  
      Example:
          visualize_tensors('viz1', [img1])
          visualize_tensors('viz2', [img1, img2, img3], max_outputs=max(30, BATCH))