However, the real way around this problem lies in re-factoring your code to comply to Python’s Windows-specific multiprocessing guidelines as discussed here in this 295 StackOverflow thread.
This subject is touched upon in Python 2 documentation for multiprocessing: Programming Guidelines, Windows 92. While Python 3 documentation shares similar guidelines (see here 146), the Python 2 is more explicit with Windows:

Make sure that the main module can be safely imported by a new Python interpreter without causing unintended side effects (such a starting a new process).

In short, the the idea here would be to wrap the example code inside an if __name__ == '__main__' statement as follows:
# Deep Learning with PyTorch: A 60 Minute Blitz » Training a classifier 
# Load the CIFAR10 data
import torch
import torchvision
import torchvision.transforms as transforms

# Safe DataLoader multiprocessing with Windows
if __name__ == '__main__':
    # Code to load the data with num_workers > 1

While the tutorial seems to define multiple scripts, the best way around this would be to wrap all operations in functions and then call them inside an if __name__ == '__main__' clause:
# Imports for dataset generation, training, etc

def load_datasets(...):
    # Code to load the datasets with multiple workers

def train(...):
    # Code to train the model

if __name__ == '__main__':
    load_datasets()
    train()