text: 
  
  from unittest import TestCase
  
  class GradCamTest(TestCase):
  
      @classmethod
      def setUpClass(cls):
          import torchvision.models as models
          from torchvision.datasets import FakeData,CIFAR10,STL10
          # from torch.utils.data import DataLoader
          cls.model = models.resnet18(pretrained=True)
          cls.dataset = CIFAR10('./data',download=True)# FakeData() #
  
      def test_gradcam(self):
          from metrician.explainations.image import GradCamExplainer
          from torchvision import transforms as T
          import random
          from pylab import show
          grad_cam = GradCamExplainer(self.model,'layer3')
          grad_cam( T.ToTensor()(self.dataset[0][0]).unsqueeze(0), random.randint(0,10))
  
  
      def test_full_gradcam_pipeline(self):pass
  
  
  