class_docstr: LoRA incorporated in Linear Layer. Weights of linear layer are set to be frozen per default.
  
  Parameters
  ----------
  in_features
      input dimension, set to the original linear layer input dimension LoRA is replacing.
  out_features
      output dimension, set to the original linear layer output dimension LoRA is replacing.
  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
      Dropout probability.
  fan_in_fan_out
      Set this to True if the layer to replace stores weight like (fan_in, fan_out).
  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 T(self, w):
      return w.T if self.fan_in_fan_out else w
  
imports: import math
  from typing import List
  import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 3 for calling the LoRALinear obj's underlying member method code with example usage: obj.T(w) and returns: unknown