docstr: Score.
docstr_tok: ["Score","."]
code: def score(self, ys, state, x, speech=None):
      """Score."""
      ys_mask = subsequent_mask(len(ys), device=x.device).unsqueeze(0)
      logp, state = self.forward_one_step(
          ys.unsqueeze(0),
          ys_mask,
          x.unsqueeze(0),
          speech.unsqueeze(0) if speech is not None else None,
          cache=state,
      )
      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 TransformerMDDecoder obj's underlying member method code with example usage: obj.score(ys, state, x, speech) and returns: logp,  state