code: def test_torch_net_predict_resnet(self):
      torch.random.manual_seed(1)
      pytorch_model = torchvision.models.resnet18(
          pretrained=False
      ).eval()
      zoo_model = TorchModel.from_pytorch(pytorch_model)
      zoo_model.evaluate()
  
      dummy_input = torch.ones(1, 3, 224, 224)
      pytorch_result = pytorch_model(dummy_input).data.numpy()
      zoo_result = zoo_model.forward(dummy_input.numpy())
      print(pytorch_result)
      print(zoo_result)
      assert np.allclose(
          pytorch_result, zoo_result, rtol=1.0e-6, atol=1.0e-6
      )
  
imports: import torch
  from torch import nn
  import torchvision
  import pytest
  from unittest import TestCase
  from bigdl.orca.torch import TorchModel, TorchLoss
  from bigdl.dllib.nncontext import *
  from torch.utils.data import TensorDataset, DataLoader
  from bigdl.dllib.estimator import *
  from bigdl.dllib.keras.optimizers import Adam
  from bigdl.dllib.optim.optimizer import MaxEpoch, EveryEpoch
  from bigdl.dllib.keras.metrics import Accuracy
  from bigdl.dllib.feature.common import FeatureSet
label_desc: Use image node_id 5 for calling the TestPytorch obj's underlying member method code with example usage: obj.test_torch_net_predict_resnet() without return types