code: def update_layer(
      self,
      adapter_name,
      r,
      lora_alpha,
      lora_dropout,
      init_lora_weights,
      use_rslora,
  ):
      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[adapter_name] = lora_dropout_layer
      # Actual trainable parameters
      base_layer = self.get_base_layer()
      kernel_size = base_layer.kernel_size
      stride = base_layer.stride
      padding = base_layer.padding
      self.lora_A[adapter_name] = nn.Conv2d(
          self.in_features, r, kernel_size, stride, padding, bias=False
      )
      self.lora_B[adapter_name] = nn.Conv2d(
          r, self.out_features, (1, 1), (1, 1), 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)
  
      weight = getattr(base_layer, "weight", None)
      if weight is not None:
          # the layer is already completely initialized, this is an update
          self.to(base_layer.weight.device, dtype=weight.dtype)
      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 Conv2d 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