shairng the Transformation I applied in the dataset here for ref.
Data augmentation techniques to the training set:
#original training set:
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

# Shifting up/down and left/right by within 10%:

transform2 = transforms.Compose([transforms.RandomAffine(degrees=0, translate=(0.1,0.1), shear=0), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

#Rotating:

transform3 = transforms.Compose([transforms.RandomAffine(degrees=30, shear=0), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

#Horizontal Flipping:

transform4 = transforms.Compose([transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

#Adding small Gaussian noise:

class AddGaussianNoise(object):

    def __init__(self, mean=0., std=1.):

        self.std = std

        self.mean = mean

        

    def __call__(self, tensor):

        return tensor + torch.randn(tensor.size()) * self.std + self.mean

    

    def __repr__(self):

        return self.__class__.__name__ + '(mean={0}, std={1})'.format(self.mean, self.std)

transform5 = transforms.Compose([AddGaussianNoise(0.,1.), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

#transform6 = transforms.Compose([transforms.RandomAffine(degrees=0, translate=(0.1,0.1), shear=0), transforms.RandomAffine(degrees=30, shear=0), transforms.RandomHorizontalFlip(), AddGaussianNoise(0.,1.), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

# TODO: load the CIFAR-10 dataset and build dataloader

cifar_trainset = datasets.CIFAR10(root = './data', train=True, download=True, transform = transform)

cifar_trainset2 = datasets.CIFAR10(root = './data', train=True, download=True, transform = transform2)

cifar_trainset3 = datasets.CIFAR10(root = './data', train=True, download=True, transform = transform3)

cifar_trainset4 = datasets.CIFAR10(root = './data', train=True, download=True, transform = transform4)

cifar_trainset5 = datasets.CIFAR10(root = './data', train=True, download=True, transform = transform5)

#Concatenate the 5 different Training set into 1 new training set

cifar_trainset_new = torch.utils.data.ConcatDataset([cifar_trainset, cifar_trainset2, cifar_trainset3, cifar_trainset4, cifar_trainset5])

cifar_trainloader_new = torch.utils.data.DataLoader(cifar_trainset_new, batch_size=4, shuffle=True)

cifar_trainset_new_size = len(cifar_trainset_new)

print(cifar_trainset_new_size)

#cifar_trainloader = torch.utils.data.DataLoader(cifar_trainset6, batch_size=4, shuffle=True)

#cifar_trainset6_size = len(cifar_trainset6)

#print(cifar_trainset6_size)

cifar_testset = datasets.CIFAR10(root = './data', train=False, download=True, transform = transform)

cifar_testloader = torch.utils.data.DataLoader(cifar_testset, batch_size=1, shuffle=False)

cifar_testset_size = len(cifar_testset)

print(cifar_testset_size)

print("CIFAR10 new training dataset:\n ", cifar_trainset_new)

print("CIFAR10 testing dataset:\n ", cifar_testset)