I am implementing cycle Gans  .
My dataloader is written as : class MyDataset(Dataset) :
def init(self , patch_CT, patch_PET):
‘characterizes a dataset for pytorch’
self.patch_CT = patch_CT
self.patch_PET= patch_PET
self.transforms = transforms.ToTensor
 def __len__(self):
    'denotes the total number of samples'
    return  len(self.patch_CT)
    print(len(self.patch_PET))

 def __getitem__(self,index):
    'Generates one sample of data'
    #select sample
    x = self.patch_CT[index]

    y = self.patch_PET[index]

    # Unsqueeze channel dimension
    #x = x.unsqueeze(0)
    #y = y.unsqueeze(0)
    return x,y

train_A_dataset, test_A_dataset = torch.utils.data.random_split(
patch_CT ,[int(0.7len(patch_CT)), int(len(patch_CT) - int(0.7len(patch_CT)))])
train_B_dataset, test_B_dataset = torch.utils.data.random_split(
patch_PET ,[int(0.7len(patch_PET)), int(len(patch_PET) - int(0.7len(patch_PET)))])
and for dataloader:
a_loader = torch.utils.data.DataLoader(train_A_dataset, batch_size=5, shuffle=False
b_loader = torch.utils.data.DataLoader(train_B_dataset, batch_size=5, shuffle=False)
for i,batch in enumerate((a_loader, b_loader)):
            # step
            step = epoch * min(len(a_loader), len(b_loader)) + i + 1

            # Generator Computations
            ##################################################

            set_grad([self.Da, self.Db], False)
            self.g_optimizer.zero_grad()

            a_real = Variable(batch[0])
            b_real = Variable(batch[0])
            a_real, b_real = utils.cuda([a_real, b_real])

The error I get is
a_real = Variable(batch[0])
TypeError: ‘DataLoader’ object does not support indexing
How can I solve this?
Thank you in advance.