class_docstr: The implementation of relative position encoding for 2D image feature maps.
  
  Used in :class:`RelativePositionSelfAttention`.
code: def forward(self, length_q, length_k):
      # remove the first cls token distance computation
      length_q = length_q - 1
      length_k = length_k - 1
      # init in the device directly, rather than move to device
      range_vec_q = torch.arange(
          length_q, device=self.embeddings_table_v.device
      )
      range_vec_k = torch.arange(
          length_k, device=self.embeddings_table_v.device
      )
      # compute the row and column distance
      length_q_sqrt = int(length_q**0.5)
      distance_mat_v = torch.div(
          range_vec_k[None, :], length_q_sqrt, rounding_mode="trunc"
      ) - torch.div(
          range_vec_q[:, None], length_q_sqrt, rounding_mode="trunc"
      )
      distance_mat_h = (
          range_vec_k[None, :] % length_q_sqrt
          - range_vec_q[:, None] % length_q_sqrt
      )
      # clip the distance to the range of [-length, length]
      distance_mat_clipped_v = torch.clamp(
          distance_mat_v, -self.length, self.length
      )
      distance_mat_clipped_h = torch.clamp(
          distance_mat_h, -self.length, self.length
      )
  
      # translate the distance from [1, 2 * length + 1], 0 is for the cls token
      final_mat_v = distance_mat_clipped_v + self.length + 1
      final_mat_h = distance_mat_clipped_h + self.length + 1
      # pad the 0 which represent the cls token
      final_mat_v = F.pad(final_mat_v, (1, 0, 1, 0), "constant", 0)
      final_mat_h = F.pad(final_mat_h, (1, 0, 1, 0), "constant", 0)
  
      final_mat_v = final_mat_v.long()
      final_mat_h = final_mat_h.long()
      # get the embeddings with the corresponding distance
      embeddings = (
          self.embeddings_table_v[final_mat_v]
          + self.embeddings_table_h[final_mat_h]
      )
  
      return embeddings
  
imports: from __future__ import annotations
  from typing import Tuple, cast, Any, Dict
  import torch
  import torch.nn
  import torch.nn.functional
  import nni
  from nni.mutable import Categorical, MutableExpression, frozen_context, ensure_frozen, Mutable
  from nni.nas.nn.pytorch import ParametrizedModule, ModelSpace, MutableModule, Repeat, MutableConv2d, MutableLinear, MutableLayerNorm
  from nni.nas.space import current_model
  from nni.nas.oneshot.pytorch.supermodule.operation import MixedOperation
  from nni.nas.profiler.pytorch.flops import FlopsResult
  from nni.nas.profiler.pytorch.utils import MutableShape, ShapeTensor, profiler_leaf_module
  from .utils.pretrained import load_pretrained_weight
  from .utils.nn import DropPath
label_desc: Use image node_id 2 for calling the RelativePosition2D obj's underlying member method code with example usage: obj.forward(length_q, length_k) and returns: embeddings