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: Create batch of decoder hidden states given a list of new states.
  
  Args:
      new_states: Decoder hidden states. [B x [N x Dict]]
  
  Returns:
      : Decoder hidden states. [N x Dict]
docstr_tok: ["Create","batch","of","decoder","hidden","states","given","a","list","of","new","states",".","Args",":","new_states",":","Decoder","hidden","states",".","[","B","x","[","N","x","Dict","]","]","Returns",":",":","Decoder","hidden","states",".","[","N","x","Dict","]"]
code: def create_batch_states(
      self,
      new_states: List[List[Dict[str, torch.Tensor]]],
  ) -> List[Dict[str, torch.Tensor]]:
      """Create batch of decoder hidden states given a list of new states.
  
      Args:
          new_states: Decoder hidden states. [B x [N x Dict]]
  
      Returns:
          : Decoder hidden states. [N x Dict]
  
      """
      return [
          {
              "ema_state": torch.stack(
                  [state[n_b]["ema_state"] for state in new_states]
              ),
              "prev_key": self.stack_qk_states(
                  [state[n_b]["prev_key"] for state in new_states],
                  self.mega_att_k_size,
              ),
              "prev_value": self.stack_qk_states(
                  [state[n_b]["prev_value"] for state in new_states],
                  self.mega_att_v_size,
              ),
          }
          for n_b in range(self.num_blocks)
      ]
  
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 10 for calling the MEGADecoder obj's underlying member method code with example usage: obj.create_batch_states(new_states) and returns: unknown