code: def sample(model, model_path, output_name="gen/gen"):
      pred = PredictConfig(
          session_init=SmartInit(model_path),
          model=model,
          input_names=["z"],
          output_names=[output_name, "z"],
      )
      pred = SimpleDatasetPredictor(
          pred, RandomZData((100, args.z_dim))
      )
      for o in pred.get_result():
          o = o[0] + 1
          o = o * 128.0
          o = np.clip(o, 0, 255)
          o = o[:, :, :, ::-1]
          stack_patches(o, nr_row=10, nr_col=10, viz=True)
  
imports: import argparse
  import glob
  import numpy
  import os
  import tensorflow
  from tensorpack import *
  from tensorpack.tfutils.scope_utils import auto_reuse_variable_scope
  from tensorpack.utils.viz import stack_patches
  from GAN import GANModelDesc, GANTrainer, RandomZData
label_desc: Use image node_id 3 for calling a global function with example usage: sample(model, model_path, output_name) without return types