class_docstr: MEGA decoder module.
  
  Based on https://arxiv.org/pdf/2209.10655.pdf.
  
  Args:
      vocab_size: Vocabulary size.
      block_size: Input/Output size.
      linear_size: NormalizedPositionwiseFeedForward hidden size.
      qk_size: Shared query and key size for attention module.
      v_size: Value size for attention module.
      num_heads: Number of EMA heads.
      rel_pos_bias: Type of relative position bias in attention module.
      max_positions: Maximum number of position for RelativePositionBias.
      truncation_length: Maximum length for truncation in EMA module.
      normalization_type: Normalization layer type.
      normalization_args: Normalization layer arguments.
      activation_type: Activation function type.
      activation_args: Activation function arguments.
      chunk_size: Chunk size for attention computation (-1 = full context).
      num_blocks: Number of MEGA blocks.
      dropout_rate: Dropout rate for MEGA internal modules.
      embed_dropout_rate: Dropout rate for embedding layer.
      att_dropout_rate: Dropout rate for the attention module.
      ema_dropout_rate: Dropout rate for the EMA module.
      ffn_dropout_rate: Dropout rate for the feed-forward module.
      embed_pad: Embedding padding symbol ID.
docstr: One-step forward hypothesis.
  
  Args:
      label_sequence: Current label sequence.
      states: Decoder hidden states. (??)
  
  Returns:
      : Decoder output sequence. (D_dec)
      states: Decoder hidden states. (??)
docstr_tok: ["One-step","forward","hypothesis",".","Args",":","label_sequence",":","Current","label","sequence",".","states",":","Decoder","hidden","states",".","(","?","?",")","Returns",":",":","Decoder","output","sequence",".","(","D_dec",")","states",":","Decoder","hidden","states",".","(","?","?",")"]
code: def score(
      self,
      label_sequence: List[int],
      states: List[Dict[str, torch.Tensor]],
  ) -> Tuple[torch.Tensor, List[Dict[str, torch.Tensor]]]:
      """One-step forward hypothesis.
  
      Args:
          label_sequence: Current label sequence.
          states: Decoder hidden states. (??)
  
      Returns:
          : Decoder output sequence. (D_dec)
          states: Decoder hidden states. (??)
  
      """
      str_labels = "_".join(map(str, label_sequence))
  
      if str_labels in self.score_cache:
          out, states = self.score_cache[str_labels]
      else:
          label = torch.full(
              (1, 1),
              label_sequence[-1],
              dtype=torch.long,
              device=self.device,
          )
  
          out, states = self.inference(label, states=states)
  
          self.score_cache[str_labels] = (out, states)
  
      return out[0], states
  
imports: import math
  from typing import Dict, List, Optional, Tuple
  import torch
  from typeguard import check_argument_types
  from espnet2.asr_transducer.activation import get_activation
  from espnet2.asr_transducer.beam_search_transducer import Hypothesis
  from espnet2.asr_transducer.decoder.abs_decoder import AbsDecoder
  from espnet2.asr_transducer.decoder.blocks.mega import MEGA
  from espnet2.asr_transducer.decoder.modules.mega.feed_forward import NormalizedPositionwiseFeedForward
  from espnet2.asr_transducer.normalization import get_normalization
label_desc: Use image node_id 5 for calling the MEGADecoder obj's underlying member method code with example usage: obj.score(label_sequence, states) and returns: out,  states