Thank @eqy, It works now.
now, if you continue running, I have two problems:

In the evaluation of the model, with 20 classes… it indicates a problem with “out of bounds”.

    classes = ['plane', 'car', 'bird', 'cat','deer', 'dog', 'frog', 'horse', 'ship', 'truck','T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']

    correct = [0]*len(classes)
    total = [0]*len(classes)
    acc = [0]*len(classes)

    cnn.eval()
    with tqdm.notebook.tqdm(total=len(testloader), unit='batch', desc=f'Evaluation', 
                            position=100, leave=True) as pbar:  

          for X, Y in testloader:
            X, Y = X.cuda(), Y.cuda()                
            X = X.to(device)
            Y = Y.to(device)

            _, Y_ = torch.max(cnn(X.cuda()).data, 1)

            for y, y_ in zip(Y, Y_):
              if y == y_:
                  correct[y] += 1
              total[y] += 1
              acc[y] = correct[y]/total[y]

            pbar.set_postfix(accuracy=sum(acc)/len(classes))
            pbar.update()   

    #--# Results
    for class_name,class_acc in zip(classes,acc):
        print(f'Accuracy para la clase {str(class_name):10s}: {class_acc:.2f}')
    print(f'Accuracy promedio (balanceado): {sum(acc)/len(classes):.2f}')

The evaluation-output is:
---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-48-a5f1fa2cc744> in <module>
      8                         position=100, leave=True) as pbar:  
      9 
---> 10       for X, Y in testloader:
     11         X, Y = X.cuda(), Y.cuda()
     12         X = X.to(device)

c:\users\xyz\anaconda3\envs\envgene\lib\site-packages\torch\utils\data\dataloader.py in __next__(self)
    515             if self._sampler_iter is None:
    516                 self._reset()
--> 517             data = self._next_data()
    518             self._num_yielded += 1
    519             if self._dataset_kind == _DatasetKind.Iterable and \

c:\users\xyz\anaconda3\envs\envgene\lib\site-packages\torch\utils\data\dataloader.py in _next_data(self)
    555     def _next_data(self):
    556         index = self._next_index()  # may raise StopIteration
--> 557         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration
    558         if self._pin_memory:
    559             data = _utils.pin_memory.pin_memory(data)

c:\users\xyz\anaconda3\envs\envgene\lib\site-packages\torch\utils\data\_utils\fetch.py in fetch(self, possibly_batched_index)
     42     def fetch(self, possibly_batched_index):
     43         if self.auto_collation:
---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]
     45         else:
     46             data = self.dataset[possibly_batched_index]

c:\users\xyz\anaconda3\envs\envgene\lib\site-packages\torch\utils\data\_utils\fetch.py in <listcomp>(.0)
     42     def fetch(self, possibly_batched_index):
     43         if self.auto_collation:
---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]
     45         else:
     46             data = self.dataset[possibly_batched_index]

c:\users\xyz\anaconda3\envs\envgene\lib\site-packages\torch\utils\data\dataset.py in __getitem__(self, idx)
    217         else:
    218             sample_idx = idx - self.cumulative_sizes[dataset_idx - 1]
--> 219         return self.datasets[dataset_idx][sample_idx]
    220 
    221     @property

c:\users\xyz\anaconda3\envs\envgene\lib\site-packages\torch\utils\data\dataset.py in __getitem__(self, idx)
    328 
    329     def __getitem__(self, idx):
--> 330         return self.dataset[self.indices[idx]]
    331 
    332     def __len__(self):

c:\users\xyz\anaconda3\envs\envgene\lib\site-packages\torchvision\datasets\cifar.py in __getitem__(self, index)
    111             tuple: (image, target) where target is index of the target class.
    112         """
--> 113         img, target = self.data[index], self.targets[index]
    114 
    115         # doing this so that it is consistent with all other datasets

IndexError: index 10000 is out of bounds for axis 0 with size 10000

I do not understand, what is the reason for dimension problems?

The classes function does not work the same, why is this?

trainset_1.classes
['airplane',
 'automobile',
 'bird',
 'cat',
 'deer',
 'dog',
 'frog',
 'horse',
 'ship',
 'truck']

trainset_2.classes
['T-shirt/top',
 'Trouser',
 'Pullover',
 'Dress',
 'Coat',
 'Sandal',
 'Shirt',
 'Sneaker',
 'Bag',
 'Ankle boot']

Is it required to do something in dataset before?
trainset = torch.utils.data.ConcatDataset([trainset_1, trainset_2]) 
trainset.classes

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-51-fe0a9b13bfd7> in <module>
      1 trainset = torch.utils.data.ConcatDataset([trainset_1, trainset_2])
----> 2 trainset.classes

AttributeError: 'ConcatDataset' object has no attribute 'classes'

Appreciating the comments
Greetings
K