class_docstr: VC Transformer module.
  
  This is a module of the Voice Transformer Network
  (a.k.a. VTN or Transformer-VC) described in
  `Voice Transformer Network: Sequence-to-Sequence
  Voice Conversion Using Transformer with
  Text-to-Speech Pretraining`_,
  which convert the sequence of acoustic features
  into the sequence of acoustic features.
  
  .. _`Voice Transformer Network: Sequence-to-Sequence
      Voice Conversion Using Transformer with
      Text-to-Speech Pretraining`:
      https://arxiv.org/pdf/1912.06813.pdf
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docstr: Make masks for masked self-attention.
  
  Args:
      olens (LongTensor or List): Batch of lengths (B,).
  
  Returns:
      Tensor: Mask tensor for masked self-attention.
              dtype=torch.uint8 in PyTorch 1.2-
              dtype=torch.bool in PyTorch 1.2+ (including 1.2)
  
  Examples:
      >>> olens = [5, 3]
      >>> self._target_mask(olens)
      tensor([[[1, 0, 0, 0, 0],
               [1, 1, 0, 0, 0],
               [1, 1, 1, 0, 0],
               [1, 1, 1, 1, 0],
               [1, 1, 1, 1, 1]],
              [[1, 0, 0, 0, 0],
               [1, 1, 0, 0, 0],
               [1, 1, 1, 0, 0],
               [1, 1, 1, 0, 0],
               [1, 1, 1, 0, 0]]], dtype=torch.uint8)
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code: def _target_mask(self, olens):
      """Make masks for masked self-attention.
  
      Args:
          olens (LongTensor or List): Batch of lengths (B,).
  
      Returns:
          Tensor: Mask tensor for masked self-attention.
                  dtype=torch.uint8 in PyTorch 1.2-
                  dtype=torch.bool in PyTorch 1.2+ (including 1.2)
  
      Examples:
          >>> olens = [5, 3]
          >>> self._target_mask(olens)
          tensor([[[1, 0, 0, 0, 0],
                   [1, 1, 0, 0, 0],
                   [1, 1, 1, 0, 0],
                   [1, 1, 1, 1, 0],
                   [1, 1, 1, 1, 1]],
                  [[1, 0, 0, 0, 0],
                   [1, 1, 0, 0, 0],
                   [1, 1, 1, 0, 0],
                   [1, 1, 1, 0, 0],
                   [1, 1, 1, 0, 0]]], dtype=torch.uint8)
  
      """
      y_masks = make_non_pad_mask(olens)
      s_masks = subsequent_mask(
          y_masks.size(-1), device=y_masks.device
      ).unsqueeze(0)
      return y_masks.unsqueeze(-2) & s_masks
  
imports: import logging
  import torch
  import torch.nn.functional
  from espnet.nets.pytorch_backend.e2e_asr_transformer import subsequent_mask
  from espnet.nets.pytorch_backend.e2e_tts_tacotron2 import Tacotron2Loss
  from espnet.nets.pytorch_backend.e2e_tts_transformer import TTSPlot
  from espnet.nets.pytorch_backend.e2e_tts_transformer import GuidedMultiHeadAttentionLoss
  from espnet.nets.pytorch_backend.nets_utils import make_non_pad_mask
  from espnet.nets.pytorch_backend.tacotron2.decoder import Postnet
  from espnet.nets.pytorch_backend.tacotron2.decoder import Prenet
  from espnet.nets.pytorch_backend.tacotron2.encoder import Encoder
  from espnet.nets.pytorch_backend.transformer.attention import MultiHeadedAttention
  from espnet.nets.pytorch_backend.transformer.decoder import Decoder
  from espnet.nets.pytorch_backend.transformer.embedding import PositionalEncoding, ScaledPositionalEncoding
  from espnet.nets.pytorch_backend.transformer.encoder import Encoder
  from espnet.nets.pytorch_backend.transformer.initializer import initialize
  from espnet.nets.tts_interface import TTSInterface
  from espnet.utils.cli_utils import strtobool
  from espnet.utils.fill_missing_args import fill_missing_args
label_desc: Use image node_id 11 for calling the Transformer obj's underlying member method code with example usage: obj._target_mask(olens) and returns: unknown