class_docstr: Implementation of conditional positional encoding.
  
  For more details refer to paper:
  `Conditional Positional Encodings for Vision Transformers <https://arxiv.org/pdf/2102.10882.pdf>`_
  
  In our implementation, we can reparameterize this module to eliminate a skip connection.
class_docstr_tok: ["Implementation","of","conditional","positional","encoding",".","For","more","details","refer","to","paper",":","`","Conditional","Positional","Encodings","for","Vision","Transformers","<","https",":","\/\/arxiv.org\/pdf\/2102.10882.pdf",">","`","_","In","our","implementation",",","we","can","reparameterize","this","module","to","eliminate","a","skip","connection","."]
code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      if hasattr(self, "reparam_conv"):
          x = self.reparam_conv(x)
          return x
      else:
          x = self.pe(x) + x
          return x
  
imports: import os
  import copy
  from functools import partial
  from typing import List, Tuple, Optional, Union
  import torch
  import torch.nn
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.models.layers import DropPath, trunc_normal_
  from timm.models.registry import register_model
  from models.modules.mobileone import MobileOneBlock
  from models.modules.replknet import ReparamLargeKernelConv
label_desc: Use image node_id 2 for calling the RepCPE obj's underlying member method code with example usage: obj.forward(x) and returns: x, x