import torch
import torchvision
from torch.utils.data import Dataset,DataLoader
from sklearn import preprocessing
class youtubeFacial(Dataset):
def __init__(self,transform=None):
    
    raw_data = pd.read_csv('../input/fpoints/training.csv')
    raw_data.fillna(0,inplace = True)
    self.n_samples = raw_data.shape[0]
    raw_data['Image'] = raw_data['Image'].apply(lambda img:  np.fromstring(img, sep = ' '))
    X = np.vstack(raw_data['Image'].values)
    X = X.reshape(-1, 1, 96, 96)
    X = X.astype('float64')
    self.X = torch.Tensor(X)
    Y = raw_data[raw_data.columns[:-1]].values
    Y = Y.astype('float64')
    self.Y = torch.Tensor(Y) 
    
    self.transform = transform
    
def __getitem__(self,index):
    return self.transform(self.X[index]),self.transform(self.X[index])

    

def __len__(self):
    return self.n_samples

and
from torchvision import transforms, utils
transform = transforms.Compose([
transforms.Normalize((0.5), (0.5)),
transforms.ToTensor()])
dataset = youtubeFacial(transform)
train_loader = DataLoader(dataset = dataset,batch_size = 4,shuffle = True)
but I am encountering this problem in the title during at the  training time. how can i solve this problem ?