class_docstr: Factorized attention with convolutional relative position encoding class. 
class_docstr_tok: ["Factorized","attention","with","convolutional","relative","position","encoding","class","."]
code: def forward(self, x, size: Tuple[int, int]):
      B, N, C = x.shape
  
      # Generate Q, K, V.
      qkv = (
          self.qkv(x)
          .reshape(B, N, 3, self.num_heads, C // self.num_heads)
          .permute(2, 0, 3, 1, 4)
      )
      q, k, v = qkv.unbind(0)  # [B, h, N, Ch]
  
      # Factorized attention.
      k_softmax = k.softmax(dim=2)
      factor_att = k_softmax.transpose(-1, -2) @ v
      factor_att = q @ factor_att
  
      # Convolutional relative position encoding.
      crpe = self.crpe(q, v, size=size)  # [B, h, N, Ch]
  
      # Merge and reshape.
      x = self.scale * factor_att + crpe
      x = x.transpose(1, 2).reshape(
          B, N, C
      )  # [B, h, N, Ch] -> [B, N, h, Ch] -> [B, N, C]
  
      # Output projection.
      x = self.proj(x)
      x = self.proj_drop(x)
  
      return x
  
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 FactorAttnConvRelPosEnc obj's underlying member method code with example usage: obj.forward(x, size) and returns: x