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 hypotheses.
  
  Args:
      hyps: Hypotheses.
  
  Returns:
      out:
      states:
docstr_tok: ["One-step","forward","hypotheses",".","Args",":","hyps",":","Hypotheses",".","Returns",":","out",":","states",":"]
code: def batch_score(
      self, hyps: List[Hypothesis]
  ) -> Tuple[torch.Tensor, List[Dict[str, torch.Tensor]]]:
      """One-step forward hypotheses.
  
      Args:
          hyps: Hypotheses.
  
      Returns:
          out:
          states:
  
      """
      labels = torch.tensor(
          [[h.yseq[-1]] for h in hyps],
          dtype=torch.long,
          device=self.device,
      )
      states = self.create_batch_states([h.dec_state for h in hyps])
  
      out, states = self.inference(labels, states=states)
  
      return out.squeeze(1), 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 6 for calling the MEGADecoder obj's underlying member method code with example usage: obj.batch_score(hyps) and returns: out,  states