class_docstr: (IA)^3 incorporated in a 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.
  scaling_rank
      Merging weights during inference to reduce latency.
  
  References
  ----------
  1. Liu, Haokun and Tam, Derek and Muqeeth, Mohammed and Mohta, Jay and Huang, Tenghao and Bansal, Mohit and Raffel, Colin,
  "Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning", 2022
  https://arxiv.org/pdf/2205.05638.pdf
  2. Code: https://github.com/r-three/t-few
code: def extra_repr(self):
      return "in_features={}, out_features={}, bias={}".format(
          self.in_features, self.out_features, self.bias is not None
      )
  
imports: import math
  from typing import List
  import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 5 for calling the IA3Linear obj's underlying member method code with example usage: obj.extra_repr() and returns: str