For future reference, this error can pop up if you have an exception in your dataset implementation. Instead of printing the exception stack trace, windows version will print this garbage instead.
Minimal example:
import torch
import torch.utils.data

class Dataset(torch.utils.data.Dataset):
    def __init__(self):
        super(Dataset, self).__init__()

    def __len__(self):
        return 92408

    def __getitem__(self, item):
        if item == 27003:
            raise Exception(':-)')
        return torch.rand((3, 16, 16))

def crash_test():
    d = Dataset()
    test_loader = torch.utils.data.DataLoader(d, 16, shuffle=False, num_workers=4, drop_last=False)
    batches = []
    for i, batch in enumerate(test_loader):
        # run batch through net..
        generated = batch
        batches.append(batch)
            
if __name__ == "__main__":
    crash_test()