class_docstr: Multi-Head Attention layer inheriting SequenceModule.
  
  Comparing default MHA module in ESPnet, this module returns additional dummy state
  and has step function for autoregressive inference.
  
  Args:
      n_head (int): The number of heads.
      n_feat (int): The number of features.
      dropout_rate (float): Dropout rate.
code: def step(self, query, state, memory=None, mask=None, **kwargs):
      if memory is None:
          memory = query
      return (
          self.forward(query, memory, mask=mask, **kwargs)[0].squeeze(
              1
          ),
          state,
      )
  
file_path: espnet/espnet2/asr/state_spaces/attention.py
imports: import math
  import numpy
  import torch
  from torch import nn
  from espnet2.asr.state_spaces.base import SequenceModule
label_desc: Use image node_id 5 for calling the MultiHeadedAttention obj's underlying member method code with example usage: obj.step(query, state, memory, mask) and returns: self,  state