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: Initialize MEGADecoder states.
  
  Args:
      batch_size: Batch size.
  
  Returns:
      states: Decoder hidden states. [N x Dict]
docstr_tok: ["Initialize","MEGADecoder","states",".","Args",":","batch_size",":","Batch","size",".","Returns",":","states",":","Decoder","hidden","states",".","[","N","x","Dict","]"]
code: def init_state(
      self, batch_size: int = 0
  ) -> List[Dict[str, torch.Tensor]]:
      """Initialize MEGADecoder states.
  
      Args:
          batch_size: Batch size.
  
      Returns:
          states: Decoder hidden states. [N x Dict]
  
      """
      return [
          {
              "ema_state": torch.zeros(
                  (self.output_size, self.mega_ema_num_heads),
                  device=self.device,
              ),
              "prev_key": torch.zeros(
                  (1, 1, self.mega_att_k_size), device=self.device
              ),
              "prev_value": torch.zeros(
                  (1, 1, self.mega_att_v_size), device=self.device
              ),
          }
          for _ 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 7 for calling the MEGADecoder obj's underlying member method code with example usage: obj.init_state(batch_size) and returns: unknown