class_docstr: Transfomer decoder module.
  
  Args:
      odim (int): Output diminsion.
      self_attention_layer_type (str): Self-attention layer type.
      attention_dim (int): Dimension of attention.
      attention_heads (int): The number of heads of multi head attention.
      conv_wshare (int): The number of kernel of convolution. Only used in
          self_attention_layer_type == "lightconv*" or "dynamiconv*".
      conv_kernel_length (Union[int, str]): Kernel size str of convolution
          (e.g. 71_71_71_71_71_71). Only used in self_attention_layer_type
          == "lightconv*" or "dynamiconv*".
      conv_usebias (bool): Whether to use bias in convolution. Only used in
          self_attention_layer_type == "lightconv*" or "dynamiconv*".
      linear_units (int): The number of units of position-wise feed forward.
      num_blocks (int): The number of decoder blocks.
      dropout_rate (float): Dropout rate.
      positional_dropout_rate (float): Dropout rate after adding positional encoding.
      self_attention_dropout_rate (float): Dropout rate in self-attention.
      src_attention_dropout_rate (float): Dropout rate in source-attention.
      input_layer (Union[str, torch.nn.Module]): Input layer type.
      use_output_layer (bool): Whether to use output layer.
      pos_enc_class (torch.nn.Module): Positional encoding module class.
          `PositionalEncoding `or `ScaledPositionalEncoding`
      normalize_before (bool): Whether to use layer_norm before the first block.
      concat_after (bool): 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)
docstr: Score.
docstr_tok: ["Score","."]
code: def score(self, ys, state, x):
      """Score."""
      ys_mask = subsequent_mask(len(ys), device=x.device).unsqueeze(0)
      if self.selfattention_layer_type != "selfattn":
          # TODO(karita): implement cache
          logging.warning(
              f"{self.selfattention_layer_type} does not support cached decoding."
          )
          state = None
      logp, state = self.forward_one_step(
          ys.unsqueeze(0), ys_mask, x.unsqueeze(0), cache=state
      )
      return logp.squeeze(0), state
  
imports: import logging
  from typing import Any, List, Tuple
  import torch
  from espnet.nets.pytorch_backend.nets_utils import rename_state_dict
  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 Decoder obj's underlying member method code with example usage: obj.score(ys, state, x) and returns: logp,  state