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 new token batch (required).
  
  Args:
      ys (torch.Tensor): torch.int64 prefix tokens (n_batch, ylen).
      states (List[Any]): Scorer states for prefix tokens.
      xs (torch.Tensor):
          The encoder feature that generates ys (n_batch, xlen, n_feat).
  
  Returns:
      tuple[torch.Tensor, List[Any]]: Tuple of
          batchfied scores for next token with shape of `(n_batch, n_vocab)`
          and next state list for ys.
docstr_tok: ["Score","new","token","batch","(","required",")",".","Args",":","ys","(","torch.Tensor",")",":","torch.int64","prefix","tokens","(","n_batch",",","ylen",")",".","states","(","List","[","Any","]",")",":","Scorer","states","for","prefix","tokens",".","xs","(","torch.Tensor",")",":","The","encoder","feature","that","generates","ys","(","n_batch",",","xlen",",","n_feat",")",".","Returns",":","tuple","[","torch.Tensor",",","List","[","Any","]","]",":","Tuple","of","batchfied","scores","for","next","token","with","shape","of","`","(","n_batch",",","n_vocab",")","`","and","next","state","list","for","ys","."]
code: def batch_score(
      self, ys: torch.Tensor, states: List[Any], xs: torch.Tensor
  ) -> Tuple[torch.Tensor, List[Any]]:
      """Score new token batch (required).
  
      Args:
          ys (torch.Tensor): torch.int64 prefix tokens (n_batch, ylen).
          states (List[Any]): Scorer states for prefix tokens.
          xs (torch.Tensor):
              The encoder feature that generates ys (n_batch, xlen, n_feat).
  
      Returns:
          tuple[torch.Tensor, List[Any]]: Tuple of
              batchfied scores for next token with shape of `(n_batch, n_vocab)`
              and next state list for ys.
  
      """
      # merge states
      n_batch = len(ys)
      n_layers = len(self.decoders)
      if states[0] is None:
          batch_state = None
      else:
          # transpose state of [batch, layer] into [layer, batch]
          batch_state = [
              torch.stack([states[b][i] for b in range(n_batch)])
              for i in range(n_layers)
          ]
  
      # batch decoding
      ys_mask = subsequent_mask(
          ys.size(-1), device=xs.device
      ).unsqueeze(0)
      logp, states = self.forward_one_step(
          ys, ys_mask, xs, cache=batch_state
      )
  
      # transpose state of [layer, batch] into [batch, layer]
      state_list = [
          [states[i][b] for i in range(n_layers)]
          for b in range(n_batch)
      ]
      return logp, state_list
  
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 5 for calling the Decoder obj's underlying member method code with example usage: obj.batch_score(ys, states, xs) and returns: logp,  state_list