code: def __init__(
      self, n_head, hidden_size, example_length, attn_dropout, dropout
  ):
      super().__init__()
      self.n_head = n_head
      assert hidden_size % n_head == 0
      self.d_head = hidden_size // n_head
      self.qkv_linears = nn.Linear(
          hidden_size, (2 * self.n_head + 1) * self.d_head, bias=False
      )
      self.out_proj = nn.Linear(self.d_head, hidden_size, bias=False)
  
      self.attn_dropout = nn.Dropout(attn_dropout)
      self.out_dropout = nn.Dropout(dropout)
      self.scale = self.d_head**-0.5
      self.register_buffer(
          "_mask",
          torch.triu(
              torch.full(
                  (example_length, example_length), float("-inf")
              ),
              1,
          ).unsqueeze(0),
      )
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch import Tensor
  from torch.nn import LayerNorm
  import logging
  import warnings
  from typing import Tuple, Optional
  from ..losses.pytorch import MAE
  from ..common._base_windows import BaseWindows
label_desc: Use image node_id 1 to create a new InterpretableMultiHeadAttention object from inherited base classes: nn with example: obj = InterpretableMultiHeadAttention(n_head, hidden_size, example_length, attn_dropout, dropout)