code: def cycle():
      while True:
          prefix = torch.ones((BATCH_SIZE, 1)).long().cuda()
          src = (
              torch.randint(2, NUM_TOKENS, (BATCH_SIZE, ENC_SEQ_LEN))
              .long()
              .cuda()
          )
          tgt = torch.cat((prefix, src, src), 1)
          src_mask = torch.ones(BATCH_SIZE, ENC_SEQ_LEN).bool().cuda()
          tgt_mask = torch.ones(BATCH_SIZE, tgt.shape[1]).bool().cuda()
          yield (src, tgt, src_mask, tgt_mask)
  
imports: import tqdm
  import torch
  import torch.optim
  from performer_pytorch import PerformerEncDec
  from torch.cuda.amp import autocast, GradScaler
label_desc: Use image node_id 1 for calling a global function with example usage: cycle() without return types