class_docstr: This class is designed to support the `relative position <https://arxiv.org/pdf/1803.02155v2.pdf>`__ in attention.
  
  Different from the absolute position embedding,
  the relative position embedding encodes relative distance between input tokens and learn the pairwise relations of them.
  It is commonly calculated via a look-up table with learnable parameters,
  interacting with queries and keys in self-attention modules.
  
  This class is different from PyTorch's built-in ``nn.MultiheadAttention`` in:
  
  1. It supports relative position embedding.
  2. It only supports self attention.
  3. It uses fixed dimension for each head, rather than fixed total dimension.
code: def forward(self, x):
      B, N, _ = x.shape
  
      # Infer one of head_dim and num_heads because one of them can be mutable.
      head_dim = (
          -1 if isinstance(self.head_dim, Mutable) else self.head_dim
      )
      num_heads = (
          -1 if isinstance(self.num_heads, Mutable) else self.num_heads
      )
  
      q = (
          self.q(x)
          .reshape(B, N, num_heads, head_dim)
          .permute(0, 2, 1, 3)
      )
      k = (
          self.k(x)
          .reshape(B, N, num_heads, head_dim)
          .permute(0, 2, 1, 3)
      )
      v = (
          self.v(x)
          .reshape(B, N, num_heads, head_dim)
          .permute(0, 2, 1, 3)
      )
      num_heads, head_dim = q.size(1), q.size(3)
  
      attn = (q @ k.transpose(-2, -1)) * self.scale
  
      if self.rpe:
          r_p_k = self.rel_pos_embed_k(N, N)
          attn = (
              attn
              + (
                  q.permute(2, 0, 1, 3).reshape(
                      N, num_heads * B, head_dim
                  )
                  @ r_p_k.transpose(2, 1)
              )
              .transpose(1, 0)
              .reshape(B, num_heads, N, N)
              * self.scale
          )
  
      attn = attn.softmax(dim=-1)
      attn = self.attn_drop(attn)
  
      x = (attn @ v).transpose(1, 2).reshape(B, N, num_heads * head_dim)
  
      if self.rpe:
          attn_1 = attn.permute(2, 0, 1, 3).reshape(N, B * num_heads, N)
          r_p_v = self.rel_pos_embed_v(N, N)
          # The size of attention is (B, num_heads, N, N), reshape it to (N, B*num_heads, N) and do batch matmul with
          # the relative position embedding of V (N, N, head_dim) get shape like (N, B*num_heads, head_dim). We reshape it to the
          # same size as x (B, num_heads, N, hidden_dim)
  
          x = x + (
              (attn_1 @ r_p_v)
              .transpose(1, 0)
              .reshape(B, num_heads, N, head_dim)
              .transpose(2, 1)
              .reshape(B, N, num_heads * head_dim)
          )
  
      x = self.proj(x)
      x = self.proj_drop(x)
      return x
  
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 3 for calling the RelativePositionSelfAttention obj's underlying member method code with example usage: obj.forward(x) and returns: x