class_docstr: LoRA incorporated in 2d-Convolutional Layer. Weights of convolutional layer are set to be frozen per default.
  
  Parameters
  ----------
  in_channels
       Number of channels in the input image.
  out_channels
      Number of channels produced by the convolution.
  kernel_size
      Size of the convolving kernel.
  r
      rank r of the low-rank decomposition.
  lora_alpha
      Scaling factor. Can be simply set to same value as r as initialization is scaled already.
  lora_dropout
      Adding dropout to LoRA.
  merge_weights
      Merging weights during inference to reduce latency.
  
  References
  ----------
  1. Edward J. Hu*, Yelong Shen*, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen,
  "LoRA: Low-Rank Adaptation of Large Language Models", 2021
  https://arxiv.org/abs/2106.09685
  2. Code: https://github.com/microsoft/LoRA
code: def reset_parameters(self):
      nn.Conv2d.reset_parameters(self)
      if hasattr(self, "lora_A"):
          # initialize A the same way as the default for nn.Linear and B to zero
          nn.init.kaiming_uniform_(self.lora_A, a=math.sqrt(5))
          nn.init.zeros_(self.lora_B)
  
imports: import math
  from typing import List
  import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the LoRAConv2d obj's underlying member method code with example usage: obj.reset_parameters() without return types