class_docstr: Implementation of reparameterizable conditional positional encoding.
  For more details refer to paper:
  `Conditional Positional Encodings for Vision Transformers <https://arxiv.org/pdf/2102.10882.pdf>`_
  
  Args:
      opts: Command line arguments.
      in_channels: Number of input channels.
      embed_dim: Number of embedding dimensions. Default: 768
      spatial_shape: Spatial shape of kernel for positional encoding. Default: (7, 7)
      inference_mode: Flag to instantiate block in inference mode. Default: ``False``
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docstr: Reparameterize linear branches.
docstr_tok: ["Reparameterize","linear","branches","."]
code: def reparameterize(self) -> None:
      """Reparameterize linear branches."""
      # 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.block.conv.weight.dtype,
          device=self.pe.block.conv.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.block.conv.weight
      b_final = self.pe.block.conv.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 argparse
  from typing import Optional, Tuple, Union
  import torch
  import torch.nn
  from cvnets.layers import ConvLayer2d, MultiHeadAttention, StochasticDepth
  from cvnets.layers.normalization.batch_norm import BatchNorm2d
  from cvnets.modules import BaseModule
  from cvnets.modules.mobileone_block import MobileOneBlock, RepLKBlock
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