Here is the complete code.
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import torch
import torchvision
#import torchvision.transforms as transforms
from torchvision import datasets, transforms
import matplotlib.pyplot as plt
import numpy as np
import helper

transf = transforms.Compose([transforms.Resize(255),

                             transforms.ToTensor(),

                             transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))

                            ])

datapath = 'raw-image2'
dataset = datasets.ImageFolder(datapath, transform=transf)

trains=int(0.7 * len(dataset))
tests=len(dataset)-trains

train_dataset, test_dataset =torch.utils.data.random_split(dataset, [trains, tests])  
trainload = torch.utils.data.DataLoader(train_dataset, batch_size=8, shuffle=True)
testload = torch.utils.data.DataLoader(test_dataset, batch_size = 8, shuffle=True)

trainload = torch.utils.data.DataLoader(train_dataset, batch_size = 8, shuffle=True )
testload = torch.utils.data.DataLoader(test_dataset, batch_size = 8, shuffle=True)

data_iter = iter(trainload)
images, labels = next(data_iter)
fig, axes = plt.subplots(figsize = (10,4), ncols=4)
for ii in range(4):
    ax = axes[ii]
    helper.imshow(images[ii], ax=ax, normalize=False)

This code I am using but no luck.