code: def test_kitty_pretrained(
      self, input_param, image_path, expected_label
  ):
      device = "cuda" if torch.cuda.is_available() else "cpu"
  
      # open image
      image_size = get_efficientnet_image_size(
          input_param["model_name"]
      )
      img = PIL.Image.open(image_path)
  
      # define ImageNet transforms
      tfms = torchvision.transforms.Compose(
          [
              torchvision.transforms.Resize(image_size),
              torchvision.transforms.CenterCrop(image_size),
              torchvision.transforms.ToTensor(),
              torchvision.transforms.Normalize(
                  [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]
              ),
          ]
      )
  
      # preprocess and prepare image tensor
      img = tfms(img).unsqueeze(0).to(device)
  
      # initialize a pretrained model
      net = test_pretrained_networks(
          EfficientNetBN, input_param, device
      )
  
      # run inference
      with eval_mode(net):
          result = net(img)
      pred_label = torch.argmax(result, dim=-1)
  
      # check output label
      self.assertEqual(pred_label, expected_label)
  
imports: from __future__ import annotations
  import os
  import unittest
  from typing import TYPE_CHECKING
  from unittest import skipUnless
  import torch
  from parameterized import parameterized
  from monai.networks import eval_mode
  from monai.networks.nets import BlockArgs, EfficientNetBN, EfficientNetBNFeatures, drop_connect, get_efficientnet_image_size
  from monai.utils import optional_import
  from tests.utils import skip_if_downloading_fails, skip_if_quick, test_pretrained_networks, test_script_save
label_desc: Use image node_id 3 for calling the TestEFFICIENTNET obj's underlying member method code with example usage: obj.test_kitty_pretrained(input_param, image_path, expected_label) without return types