from torchvision import datasets, transforms
import torch

BATCH_SIZE=64
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")

train_loader = torch.utils.data.DataLoader(
        datasets.MNIST('data', train=True, download=True, 
                       transform=transforms.Compose([
                           transforms.ToTensor(),
                           transforms.Normalize((0.1307,), (0.3081,))
                       ])),
        batch_size=BATCH_SIZE, shuffle=True)

test_loader = torch.utils.data.DataLoader(
        datasets.MNIST('data', train=False, transform=transforms.Compose([
                           transforms.ToTensor(),
                           transforms.Normalize((0.1307,), (0.3081,))
                       ])),
        batch_size=BATCH_SIZE, shuffle=True)
2aa7860ac1355ff91d85f697307c8fd1019f400e
<mask token>

<mask token>
BATCH_SIZE = 64
DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
train_loader = torch.utils.data.DataLoader(datasets.MNIST('data', train=
    True, download=True, transform=transforms.Compose([transforms.ToTensor(
    ), transforms.Normalize((0.1307,), (0.3081,))])), batch_size=BATCH_SIZE,
    shuffle=True)
test_loader = torch.utils.data.DataLoader(datasets.MNIST('data', train=
    False, transform=transforms.Compose([transforms.ToTensor(), transforms.
    Normalize((0.1307,), (0.3081,))])), batch_size=BATCH_SIZE, shuffle=True)

from torchvision import datasets, transforms
import torch
BATCH_SIZE = 64
DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
train_loader = torch.utils.data.DataLoader(datasets.MNIST('data', train=
    True, download=True, transform=transforms.Compose([transforms.ToTensor(
    ), transforms.Normalize((0.1307,), (0.3081,))])), batch_size=BATCH_SIZE,
    shuffle=True)
test_loader = torch.utils.data.DataLoader(datasets.MNIST('data', train=
    False, transform=transforms.Compose([transforms.ToTensor(), transforms.
    Normalize((0.1307,), (0.3081,))])), batch_size=BATCH_SIZE, shuffle=True)

from torchvision import datasets, transforms
import torch

BATCH_SIZE=64
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")

train_loader = torch.utils.data.DataLoader(
        datasets.MNIST('data', train=True, download=True, 
                       transform=transforms.Compose([
                           transforms.ToTensor(),
                           transforms.Normalize((0.1307,), (0.3081,))
                       ])),
        batch_size=BATCH_SIZE, shuffle=True)

test_loader = torch.utils.data.DataLoader(
        datasets.MNIST('data', train=False, transform=transforms.Compose([
                           transforms.ToTensor(),
                           transforms.Normalize((0.1307,), (0.3081,))
                       ])),
        batch_size=BATCH_SIZE, shuffle=True)