class_docstr: This code is based on
  https://github.com/facebookresearch/detr/blob/main/models/segmentation.py
  
  This is a 2D attention module, which only returns the attention softmax (no multiplication by value)
class_docstr_tok: ["This","code","is","based","on","https",":","\/\/github.com\/facebookresearch\/detr\/blob\/main\/models\/segmentation.py","This","is","a","2D","attention","module",",","which","only","returns","the","attention","softmax","(","no","multiplication","by","value",")"]
code: def forward(self, q, k, mask=None):
      q = self.q_proj(q)
      k = self.k_proj(k)
      bs, num_queries, n, c, h, w = (
          q.shape[0],
          q.shape[1],
          self.num_heads,
          self.hidden_dim // self.num_heads,
          k.shape[-2],
          k.shape[-1],
      )
      qh = q.reshape([bs, num_queries, n, c])
      kh = k.reshape([bs, n, c, h, w])
      # weights = paddle.einsum("bqnc,bnchw->bqnhw", qh * self.normalize_fact, kh)
      qh = qh.transpose([0, 2, 1, 3]).reshape([-1, num_queries, c])
      kh = kh.reshape([-1, c, h * w])
      weights = (
          paddle.bmm(qh * self.normalize_fact, kh)
          .reshape([bs, n, num_queries, h, w])
          .transpose([0, 2, 1, 3, 4])
      )
  
      if mask is not None:
          weights += mask
      # fix a potenial bug: https://github.com/facebookresearch/detr/issues/247
      weights = F.softmax(weights.flatten(3), axis=-1).reshape(
          weights.shape
      )
      weights = self.dropout(weights)
      return weights
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from ppdet.core.workspace import register
  import pycocotools.mask
  from ..initializer import linear_init_, constant_
  from ..transformers.utils import inverse_sigmoid
label_desc: Use image node_id 2 for calling the MultiHeadAttentionMap obj's underlying member method code with example usage: obj.forward(q, k, mask) and returns: weights