Question regarding sampling of Transition pairs in DQN tutorial

Continuing the discussion from Reinforcement Learning (DQN) tutorial bugs:

What will happen if in case all sampled states have next state as None

if len(memory) < BATCH_SIZE:
transitions = memory.sample(BATCH_SIZE)
batch = Transition(*zip(*transitions))

# Compute a mask of non-final states and concatenate the batch elements
non_final_mask = torch.tensor(tuple(map(lambda s: s is not None,
                                      batch.next_state)), device=device, dtype=torch.uint8)
non_final_next_states =[s for s in batch.next_state
                                            if s is not None])
state_batch =
action_batch =
reward_batch =

Is this is a bug?