code: def beholder_pytorch():
      for i in range(1000):
          fake_param = [
              tensor_and_name(
                  np.random.randn(128, 768, 3), "test" + str(i)
              )
              for i in range(5)
          ]
          arrays = [
              tensor_and_name(
                  np.random.randn(128, 768, 3), "test" + str(i)
              )
              for i in range(5)
          ]
          beholder = beholder_lib.Beholder(logdir=LOG_DIRECTORY)
          beholder.update(
              trainable=fake_param,
              arrays=arrays,
              frame=np.random.randn(128, 128),
          )
          time.sleep(0.1)
          print(i)
  
file_path: tensorboardX/examples/demo_beholder.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
  import tensorboardX.beholder
  import time
  from collections import namedtuple
label_desc: Use image node_id 1 for calling a global function with example usage: beholder_pytorch() without return types