class_docstr: LoRA (low-rank adaptation) followed by (IA)^3 (weight rescaling) 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.
  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.
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 7 for calling the IA3LoRALinear obj's underlying member method code with example usage: obj.extra_repr() and returns: str