Sorry for the misleading statement.
Each worker will get its base seed, so it will be alright.
If you try to sample from another library like np.random, you might encounter problems.
Have a look at this code sample:
class MyDataset(Dataset):
    def __init__(self):
        self.data = torch.randn(10, 1)
        
    def __getitem__(self, index):
        print("numpy random: ", np.random.randint(0, 10, size=1))
        print("pytorch random: ", torch.randint(0, 10, (1,)))
        x = self.data[index]
        return x
    
    def __len__(self):
        return len(self.data)
    
    
dataset = MyDataset()
loader = DataLoader(dataset, num_workers=2)

for batch_idx, data in enumerate(loader):
    print("batch idx {}".format(batch_idx))