code: def __init__(
      self,
      base_layer: nn.Module,
      adapter_name: str,
      fan_in_fan_out: bool = False,  # Set this to True if the layer to replace stores weight like (fan_in, fan_out)
      is_feedforward: bool = False,  # Set to True if the layer is treated as a feedforward layer
      init_ia3_weights: bool = True,
      **kwargs,
  ) -> None:
      super().__init__()
      IA3Layer.__init__(self, base_layer, is_feedforward=is_feedforward)
      self.fan_in_fan_out = fan_in_fan_out
      self._active_adapter = adapter_name
  
      self.update_layer(adapter_name, init_ia3_weights)
  
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 1 to create a new Conv2d object from inherited base classes: nn, IA3Layer with example: obj = Conv2d(base_layer, adapter_name, fan_in_fan_out, is_feedforward, init_ia3_weights)