code: def LoraLayer_update_layer(
      self,
      adapter_name,
      r,
      lora_alpha,
      lora_dropout,
      init_lora_weights,
      use_rslora=False,
  ):
      # 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 = torch.nn.Dropout(p=lora_dropout)
      else:
          lora_dropout_layer = torch.nn.Identity()
  
      self.lora_dropout.update(
          torch.nn.ModuleDict({adapter_name: lora_dropout_layer})
      )
      # Actual trainable parameters
      self.lora_A[adapter_name] = torch.nn.Linear(
          self.in_features, r, bias=False
      )
      self.lora_B[adapter_name] = torch.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:
              # [INCORRECT code]
              #
              # 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)
              self.to(weight.device, non_blocking=True)
              break
      self.set_adapter(self.active_adapters)
  
file_path: unsloth/unsloth/models/_utils.py
imports: import torch
  from typing import Union, Optional, List, Any, Callable
  import numpy
  import warnings
  import gc
  import bitsandbytes
  from transformers.models.llama.modeling_llama import logger
  from transformers import AutoTokenizer
  from platform import system
  import xformers.ops.fmha
  from xformers import __version__
  from peft.tuners.lora.layer import LoraLayer
  from peft.tuners.lora import LoraLayer
label_desc: Use image node_id 4 for calling a global function with example usage: LoraLayer_update_layer(adapter_name, r, lora_alpha, lora_dropout, init_lora_weights, use_rslora) without return types