code: def forward(
      self, x: torch.Tensor, *args: Any, **kwargs: Any
  ) -> torch.Tensor:
      dtype = 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:
          ia3_scaling = 1
          for active_adapter in self.active_adapters:
              if active_adapter not in self.ia3_l.keys():
                  continue
              dtype = self.ia3_l[active_adapter].dtype
              ia3_scaling *= self.ia3_l[active_adapter]
  
          if self.is_feedforward:
              x = x.to(dtype)
              # TODO: weight.dtype can be != self.ia3_l[self.active_adapters].dtype
              # e.g. bf16 vs fp32. Is that okay?
              interm = (x * ia3_scaling).to(
                  self.get_base_layer().weight.dtype
              )
              result = self.base_layer(interm, *args, **kwargs)
          else:
              result = self.base_layer(x, *args, **kwargs)
              result = result.to(dtype) * ia3_scaling
  
      result = result.to(previous_dtype)
      return result
  
imports: import warnings
  from typing import Any, List, Optional
  import torch
  import torch.nn
  from transformers.pytorch_utils import Conv1D
  from peft.tuners.tuners_utils import BaseTunerLayer
  from peft.utils import transpose
label_desc: Use image node_id 5 for calling the Conv2d obj's underlying member method code with example usage: obj.forward(x) and returns: result