docstr: Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`.
  Args:
      attn (Tensor): attention map.
      q (Tensor): query q in the attention layer with shape (B, q_h * q_w, C).
  Returns:
      attn (Tensor): attention map with added relative positional embeddings.
code: def add_decomposed_rel_pos(self, attn, q, h, w):
      """
      Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`.
      Args:
          attn (Tensor): attention map.
          q (Tensor): query q in the attention layer with shape (B, q_h * q_w, C).
      Returns:
          attn (Tensor): attention map with added relative positional embeddings.
      """
      Rh = self.get_rel_pos(h, self.rel_pos_h)
      Rw = self.get_rel_pos(w, self.rel_pos_w)
  
      B, _, dim = q.shape
      r_q = q.reshape([B, h, w, dim])
      # bhwc, hch->bhwh1
      # bwhc, wcw->bhw1w
      rel_h = paddle.einsum("bhwc,hkc->bhwk", r_q, Rh).unsqueeze(-1)
      rel_w = paddle.einsum("bhwc,wkc->bhwk", r_q, Rw).unsqueeze(-2)
  
      attn = attn.reshape([B, h, w, h, w]) + rel_h + rel_w
      return attn.reshape([B, h * w, h * w])
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  import numpy
  import math
  from paddle import ParamAttr
  from paddle.regularizer import L2Decay
  from paddle.nn.initializer import Constant, TruncatedNormal
  from ppdet.modeling.shape_spec import ShapeSpec
  from ppdet.core.workspace import register, serializable
  from .transformer_utils import zeros_, DropPath, Identity, window_partition, window_unpartition
  from ..initializer import linear_init_
label_desc: Use image node_id 4 for calling the Attention obj's underlying member method code with example usage: obj.add_decomposed_rel_pos(attn, q, h, w) and returns: attn