code: def create_torch_cnn_models():
      for model_name in classification_model_list:
          if model_name == "inception_v3":
              x = torch.randn(1, 3, 299, 299)
          else:
              x = torch.randn(1, 3, 224, 224)
          print("Creating model", model_name)
          model = Test_Model_Classification(model_name).create_model()
          model.eval()
          y = model(x)
          save_path = "test_models/" + model_name + ".pt"
          torch.save(model, save_path)
          print("Created Pytorch model saved to", save_path)
  
      for model_name in segmentation_model_list:
          x = torch.randn(1, 3, 224, 224)
          print("Creating model", model_name)
          model = Test_Model_Segmentation(model_name).create_model()
          model.eval()
          y = model(x)
          save_path = "test_models/" + model_name + ".pt"
          torch.save(model, save_path)
          print("Created Pytorch model saved to", save_path)
  
      for model_name in video_model_list:
          x = torch.randn(1, 3, 16, 112, 112)
          print("Creating model", model_name)
          model = Test_Model_Video(model_name).create_model()
          model.eval()
          y = model(x)
          save_path = "test_models/" + model_name + ".pt"
          torch.save(model, save_path)
          print("Created Pytorch model saved to", save_path)
  
imports: import torchvision.models
  import torch
  import torch.nn
  import tensorflow.keras.applications
  import tensorflow
  from tensorflow import keras
  from tensorflow.keras import layers
  from tensorflow.python.util import deprecation
  import string
label_desc: Use image node_id 1 for calling a global function with example usage: create_torch_cnn_models() without return types