class_docstr: Convolutional relative position encoding. 
class_docstr_tok: ["Convolutional","relative","position","encoding","."]
code: def forward(self, q, v, size: Tuple[int, int]):
      B, num_heads, N, C = q.shape
      H, W = size
      _assert(N == 1 + H * W, "")
  
      # Convolutional relative position encoding.
      q_img = q[:, :, 1:, :]  # [B, h, H*W, Ch]
      v_img = v[:, :, 1:, :]  # [B, h, H*W, Ch]
  
      v_img = v_img.transpose(-1, -2).reshape(B, num_heads * C, H, W)
      v_img_list = torch.split(
          v_img, self.channel_splits, dim=1
      )  # Split according to channels
      conv_v_img_list = []
      for i, conv in enumerate(self.conv_list):
          conv_v_img_list.append(conv(v_img_list[i]))
      conv_v_img = torch.cat(conv_v_img_list, dim=1)
      conv_v_img = conv_v_img.reshape(B, num_heads, C, H * W).transpose(
          -1, -2
      )
  
      EV_hat = q_img * conv_v_img
      EV_hat = F.pad(EV_hat, (0, 0, 1, 0, 0, 0))  # [B, h, N, Ch].
      return EV_hat
  
imports: from functools import partial
  from typing import Tuple, List, Union
  import torch
  import torch.nn
  import torch.nn.functional
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import PatchEmbed, Mlp, DropPath, to_2tuple, trunc_normal_, _assert, LayerNorm
  from ._builder import build_model_with_cfg
  from ._registry import register_model, generate_default_cfgs
label_desc: Use image node_id 2 for calling the ConvRelPosEnc obj's underlying member method code with example usage: obj.forward(q, v, size) and returns: EV_hat