class_docstr: Base class of attention modules
  *NOTE*: d_hidden must be divisible by n_head
  
  Parameters
  ----------
  d_hidden : int
      hidden dimension
  n_head : int, optional
      number of attention heads, by default 1
  bias : bool, optional
      add bias term in input and output projections, by default True
  dropout : float, optional
      dropout rate, by default 0.0
  temperature : float, optional
      softmax temperature, by default 1.0
class_docstr_tok: ["Base","class","of","attention","modules","*","NOTE","*",":","d_hidden","must","be","divisible","by","n_head","Parameters","--","--","--","--","--","d_hidden",":","int","hidden","dimension","n_head",":","int",",","optional","number","of","attention","heads",",","by","default","1","bias",":","bool",",","optional","add","bias","term","in","input","and","output","projections",",","by","default","True","dropout",":","float",",","optional","dropout","rate",",","by","default","0.0","temperature",":","float",",","optional","softmax","temperature",",","by","default","1.0"]
code: def _compute_attn_score(
      self, q: Tensor, k: Tensor, **kwargs
  ) -> Tensor:
      raise NotImplementedError
  
file_path: gluonts/src/gluonts/nursery/daf/tslib/nn/attention/base.py
imports: from typing import Tuple, Optional
  import math
  import torch
  from torch import Tensor
  from torch import nn
label_desc: Use image node_id 5 for calling the Attention obj's underlying member method code with example usage: obj._compute_attn_score(q, k) without return types