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.
docstr: Compute attention context vector.
  
  Args:
      value (torch.Tensor): Transformed value (#batch, n_head, time2, d_k).
      scores (torch.Tensor): Attention score (#batch, n_head, time1, time2).
      mask (torch.Tensor): Mask (#batch, 1, time2) or (#batch, time1, time2).
  
  Returns:
      torch.Tensor: Transformed value (#batch, time1, d_model)
          weighted by the attention score (#batch, time1, time2).
code: def forward_attention(self, value, scores, mask):
      """Compute attention context vector.
  
      Args:
          value (torch.Tensor): Transformed value (#batch, n_head, time2, d_k).
          scores (torch.Tensor): Attention score (#batch, n_head, time1, time2).
          mask (torch.Tensor): Mask (#batch, 1, time2) or (#batch, time1, time2).
  
      Returns:
          torch.Tensor: Transformed value (#batch, time1, d_model)
              weighted by the attention score (#batch, time1, time2).
  
      """
      n_batch = value.size(0)
      if mask is not None:
          mask = mask.unsqueeze(1).eq(0)  # (batch, 1, *, time2)
          min_value = float(
              numpy.finfo(
                  torch.tensor(0, dtype=scores.dtype).numpy().dtype
              ).min
          )
          scores = scores.masked_fill(mask, min_value)
          self.attn = torch.softmax(scores, dim=-1).masked_fill(
              mask, 0.0
          )  # (batch, head, time1, time2)
      else:
          self.attn = torch.softmax(
              scores, dim=-1
          )  # (batch, head, time1, time2)
  
      p_attn = self.dropout(self.attn)
      x = torch.matmul(p_attn, value)  # (batch, head, time1, d_k)
      x = (
          x.transpose(1, 2)
          .contiguous()
          .view(n_batch, -1, self.h * self.d_k)
      )  # (batch, time1, d_model)
  
      return self.linear_out(x)
  
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 3 for calling the MultiHeadedAttention obj's underlying member method code with example usage: obj.forward_attention(value, scores, mask) and returns: self