class_docstr: Base class of Transfomer decoder module.
  
  Args:
      vocab_size: output dim
      encoder_output_size: dimension of attention
      attention_heads: the number of heads of multi head attention
      linear_units: the number of units of position-wise feed forward
      num_blocks: the number of decoder blocks
      dropout_rate: dropout rate
      self_attention_dropout_rate: dropout rate for attention
      input_layer: input layer type
      use_output_layer: whether to use output layer
      pos_enc_class: PositionalEncoding or ScaledPositionalEncoding
      normalize_before: whether to use layer_norm before the first block
      concat_after: whether to concat attention layer's input and output
          if True, additional linear will be applied.
          i.e. x -> x + linear(concat(x, att(x)))
          if False, no additional linear will be applied.
          i.e. x -> x + att(x)
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docstr: Score.
docstr_tok: ["Score","."]
code: def score(self, ys, state, x, return_hs=False):
      """Score."""
      ys_mask = subsequent_mask(len(ys), device=x.device).unsqueeze(0)
      if return_hs:
          (logp, hs), state = self.forward_one_step(
              ys.unsqueeze(0),
              ys_mask,
              x.unsqueeze(0),
              cache=state,
              return_hs=return_hs,
          )
          return logp.squeeze(0), hs, state
      else:
          logp, state = self.forward_one_step(
              ys.unsqueeze(0),
              ys_mask,
              x.unsqueeze(0),
              cache=state,
              return_hs=return_hs,
          )
          return logp.squeeze(0), state
  
imports: from typing import Any, List, Sequence, Tuple
  import torch
  from typeguard import check_argument_types
  from espnet2.asr.decoder.abs_decoder import AbsDecoder
  from espnet.nets.pytorch_backend.nets_utils import make_pad_mask
  from espnet.nets.pytorch_backend.transformer.attention import MultiHeadedAttention
  from espnet.nets.pytorch_backend.transformer.decoder_layer import DecoderLayer
  from espnet.nets.pytorch_backend.transformer.dynamic_conv import DynamicConvolution
  from espnet.nets.pytorch_backend.transformer.dynamic_conv2d import DynamicConvolution2D
  from espnet.nets.pytorch_backend.transformer.embedding import PositionalEncoding
  from espnet.nets.pytorch_backend.transformer.layer_norm import LayerNorm
  from espnet.nets.pytorch_backend.transformer.lightconv import LightweightConvolution
  from espnet.nets.pytorch_backend.transformer.lightconv2d import LightweightConvolution2D
  from espnet.nets.pytorch_backend.transformer.mask import subsequent_mask
  from espnet.nets.pytorch_backend.transformer.positionwise_feed_forward import PositionwiseFeedForward
  from espnet.nets.pytorch_backend.transformer.repeat import repeat
  from espnet.nets.scorer_interface import BatchScorerInterface
label_desc: Use image node_id 4 for calling the BaseTransformerDecoder obj's underlying member method code with example usage: obj.score(ys, state, x, return_hs) and returns: logp,  hs,  state, logp,  state