code: def update_layer(
      self,
      adapter_name,
      r,
      lora_alpha,
      lora_dropout,
      init_lora_weights,
      use_rslora,
  ):
      # This code works for linear layers, override for other layer types
      if r <= 0:
          raise ValueError(
              f"`r` should be a positive integer value but the value passed is {r}"
          )
  
      self.r[adapter_name] = r
      self.lora_alpha[adapter_name] = lora_alpha
      if lora_dropout > 0.0:
          lora_dropout_layer = nn.Dropout(p=lora_dropout)
      else:
          lora_dropout_layer = nn.Identity()
  
      self.lora_dropout.update(
          nn.ModuleDict({adapter_name: lora_dropout_layer})
      )
      # Actual trainable parameters
      self.lora_A[adapter_name] = nn.Linear(
          self.in_features, r, bias=False
      )
      self.lora_B[adapter_name] = nn.Linear(
          r, self.out_features, bias=False
      )
      if use_rslora:
          self.scaling[adapter_name] = lora_alpha / math.sqrt(r)
      else:
          self.scaling[adapter_name] = lora_alpha / r
  
      if init_lora_weights == "loftq":
          self.loftq_init(adapter_name)
      elif init_lora_weights:
          self.reset_lora_parameters(adapter_name, init_lora_weights)
  
      # check weight and qweight (for GPTQ)
      for weight_name in ("weight", "qweight"):
          weight = getattr(self.get_base_layer(), weight_name, None)
          if weight is not None:
              # the layer is already completely initialized, this is an update
              if (
                  weight.dtype.is_floating_point
                  or weight.dtype.is_complex
              ):
                  self.to(weight.device, dtype=weight.dtype)
              else:
                  self.to(weight.device)
              break
      self.set_adapter(self.active_adapters)
  
imports: import math
  import warnings
  from typing import Any, List, Optional, Union
  import torch
  import torch.nn
  import torch.nn.functional
  from transformers.pytorch_utils import Conv1D
  from peft.tuners.tuners_utils import BaseTunerLayer
  from peft.utils.other import transpose
  from .config import LoraConfig
label_desc: Use image node_id 2 for calling the LoraLayer obj's underlying member method code with example usage: obj.update_layer(adapter_name, r, lora_alpha, lora_dropout, init_lora_weights, use_rslora) without return types