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 reparameterize(self) -> None:
      # Build equivalent Id tensor
      input_dim = self.in_channels // self.groups
      kernel_value = torch.zeros(
          (
              self.in_channels,
              input_dim,
              self.spatial_shape[0],
              self.spatial_shape[1],
          ),
          dtype=self.pe.weight.dtype,
          device=self.pe.weight.device,
      )
      for i in range(self.in_channels):
          kernel_value[
              i,
              i % input_dim,
              self.spatial_shape[0] // 2,
              self.spatial_shape[1] // 2,
          ] = 1
      id_tensor = kernel_value
  
      # Reparameterize Id tensor and conv
      w_final = id_tensor + self.pe.weight
      b_final = self.pe.bias
  
      # Introduce reparam conv
      self.reparam_conv = nn.Conv2d(
          in_channels=self.in_channels,
          out_channels=self.embed_dim,
          kernel_size=self.spatial_shape,
          stride=1,
          padding=int(self.spatial_shape[0] // 2),
          groups=self.embed_dim,
          bias=True,
      )
      self.reparam_conv.weight.data = w_final
      self.reparam_conv.bias.data = b_final
  
      for para in self.parameters():
          para.detach_()
      self.__delattr__("pe")
  
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 3 for calling the RepCPE obj's underlying member method code with example usage: obj.reparameterize() without return types