code: def buffered_future_mask(self, tensor):
      tensor = tensor.transpose(0, 1)
      dim = tensor.size(0)
      # self._future_mask.device != tensor.device is not working in TorchScript. This is a workaround.
      if (
          self._future_mask.size(0) == 0
          or (not self._future_mask.device == tensor.device)
          or self._future_mask.size(0) < dim
      ):
          self._future_mask = torch.triu(
              utils.fill_with_neg_inf(torch.zeros([dim, dim])), 1
          )
      self._future_mask = self._future_mask.to(tensor)
      return self._future_mask[:dim, :dim]
  
imports: import torch
  import torch.nn
  from fairseq import utils
  from fairseq.models import FairseqEncoder, FairseqEncoderDecoderModel, FairseqIncrementalDecoder, register_model, register_model_architecture
  from fairseq.models.fairseq_encoder import EncoderOut
  from fairseq.modules import LayerNorm
  from lightseq.training.ops.pytorch.layer_base import TransformerEmbeddingLayerBase
  from lightseq.training.ops.pytorch.quantization import QuantLinear, disable_quant, enable_quant, ptq_mode, qat_mode
label_desc: Use image node_id 6 for calling the LSTransformerDecoder obj's underlying member method code with example usage: obj.buffered_future_mask(tensor) and returns: self