code: def generate(
      self,
      tiled_encoder_out: Dict[str, Tensor],
      tgt_tokens: torch.Tensor,
  ):
      # One step Generation
      pad_mask = tgt_tokens.eq(self.trg_vocab.pad_idx)
  
      (
          tgt_tokens,
          token_probs,
          token_logits,
      ) = self.generate_non_autoregressive(
          tiled_encoder_out, tgt_tokens
      )
      tgt_tokens[pad_mask] = torch.tensor(
          self.trg_vocab.pad_idx, device=tgt_tokens.device
      ).long()
      token_probs[pad_mask] = torch.tensor(
          1.0, device=token_probs.device, dtype=token_probs.dtype
      )
      return tgt_tokens, token_probs, token_logits
  
imports: from enum import Enum
  from typing import Dict, Optional, Tuple
  import torch
  from pytext.common.constants import TORCH_VERSION
  from pytext.config import ConfigBase
  from pytext.models.module import Module
  from pytext.torchscript.vocab import ScriptVocabulary
  from torch import Tensor
label_desc: Use image node_id 7 for calling the MaskedSequenceGenerator obj's underlying member method code with example usage: obj.generate(tiled_encoder_out, tgt_tokens) and returns: tgt_tokens,  token_probs,  token_logits