code: def visualize_conv1_weights(filters):
      ctx = get_current_tower_context()
      if not ctx.is_main_training_tower:
          return
      with tf.name_scope("visualize_conv1"):
          filters = tf.reshape(filters, [11, 11, 3, 8, 12])
          filters = tf.transpose(
              filters, [3, 0, 4, 1, 2]
          )  # 8,11,12,11,3
          filters = tf.reshape(filters, [1, 88, 132, 3])
      tf.summary.image(
          "visualize_conv1", filters, max_outputs=1, collections=["AAA"]
      )
  
file_path: tensorpack/examples/ImageNetModels/alexnet.py
imports: import argparse
  import numpy
  import os
  import cv2
  from tensorpack import tfv1
  from tensorpack import *
  from tensorpack.dataflow import imgaug
  from tensorpack.tfutils import argscope
  from tensorpack.utils.gpu import get_num_gpu
  from imagenet_utils import ImageNetModel, get_imagenet_dataflow
label_desc: Use image node_id 1 for calling a global function with example usage: visualize_conv1_weights(filters) without return types