code: def forward(self, x: torch.Tensor, *args, **kwargs) -> torch.Tensor:
      previous_dtype = x.dtype
  
      if self.disable_adapters:
          if self.merged:
              self.unmerge()
          result = self.base_layer(x, *args, **kwargs)
      elif self.merged:
          result = self.base_layer(x, *args, **kwargs)
      else:
          result = self.base_layer(x, *args, **kwargs)
          for active_adapter in self.active_adapters:
              if active_adapter not in self.lora_A.keys():
                  continue
              lora_A = self.lora_A[active_adapter]
              lora_B = self.lora_B[active_adapter]
              dropout = self.lora_dropout[active_adapter]
              scaling = self.scaling[active_adapter]
              x = x.to(lora_A.weight.dtype)
              result += lora_B(lora_A(dropout(x))) * scaling
  
      result = result.to(previous_dtype)
      return result
  
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 6 for calling the Conv2d obj's underlying member method code with example usage: obj.forward(x) and returns: result