code: def ref_subsequent_mask(size):
      # http://nlp.seas.harvard.edu/2018/04/03/attention.html
      "Mask out subsequent positions."
      attn_shape = (1, size, size)
      mask = numpy.triu(numpy.ones(attn_shape), k=1).astype("uint8")
      return torch.from_numpy(mask) == 0
  
file_path: espnet/test/test_e2e_asr_transformer.py
imports: import argparse
  import chainer
  import numpy
  import pytest
  import torch
  import espnet.nets.chainer_backend.e2e_asr_transformer
  import espnet.nets.pytorch_backend.e2e_asr_transformer
  from espnet.nets.pytorch_backend.nets_utils import rename_state_dict
  from espnet.nets.pytorch_backend.transformer import plot
  from espnet.nets.pytorch_backend.transformer.add_sos_eos import add_sos_eos
  from espnet.nets.pytorch_backend.transformer.mask import subsequent_mask, target_mask
label_desc: Use image node_id 2 for calling a global function with example usage: ref_subsequent_mask(size) and returns: unknown