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: Stack query or key states with different lengths.
  
  Args:
      state_list: List of query or key states.
  
  Returns:
      new_state: Query/Key state.
docstr_tok: ["Stack","query","or","key","states","with","different","lengths",".","Args",":","state_list",":","List","of","query","or","key","states",".","Returns",":","new_state",":","Query\/Key","state","."]
code: def stack_qk_states(
      self, state_list: List[torch.Tensor], dim: int
  ) -> List[torch.Tensor]:
      """Stack query or key states with different lengths.
  
      Args:
          state_list: List of query or key states.
  
      Returns:
          new_state: Query/Key state.
  
      """
      max_len = max([(state.size(0)) for state in state_list])
  
      new_state = torch.zeros((len(state_list), max_len, dim))
  
      for idx, state in enumerate(state_list):
          new_state[idx, -state.size(0) :, :] = state
  
      return new_state
  
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 9 for calling the MEGADecoder obj's underlying member method code with example usage: obj.stack_qk_states(state_list, dim) and returns: new_state