docstr: Compute the delta weight for the given adapter.
  
  Args:
      adapter (str):
          The name of the adapter for which the delta weight should be computed.
docstr_tok: ["Compute","the","delta","weight","for","the","given","adapter",".","Args",":","adapter","(","str",")",":","The","name","of","the","adapter","for","which","the","delta","weight","should","be","computed","."]
code: def get_delta_weight(self, adapter) -> torch.Tensor:
      """
      Compute the delta weight for the given adapter.
  
      Args:
          adapter (str):
              The name of the adapter for which the delta weight should be computed.
      """
      device = self.lora_B[adapter].weight.device
      dtype = self.lora_A[adapter].weight.dtype
  
      # In case users wants to merge the adapter weights that are in
      # float16 while being on CPU, we need to cast the weights to float32, perform the merge and then cast back to
      # float16 because the `@` and matmul operation in general is not supported in torch + cpu + fp16.
      cast_to_fp32 = device.type == "cpu" and dtype == torch.float16
  
      weight_A = self.lora_A[adapter].weight
      weight_B = self.lora_B[adapter].weight
  
      if cast_to_fp32:
          weight_A = weight_A.float()
          weight_B = weight_B.float()
  
      # https://github.com/bmaltais/kohya_ss/blob/feb6728762a8f463d15ba936d189d4c3abfaa1ab/networks/lora.py#L117
      if self.get_base_layer().weight.size()[2:4] == (1, 1):
          # conv2d 1x1
          output_tensor = (
              weight_B.squeeze(3).squeeze(2)
              @ weight_A.squeeze(3).squeeze(2)
          ).unsqueeze(2).unsqueeze(3) * self.scaling[adapter]
      else:
          # conv2d 3x3
          output_tensor = (
              F.conv2d(
                  weight_A.permute(1, 0, 2, 3),
                  weight_B,
              ).permute(1, 0, 2, 3)
              * self.scaling[adapter]
          )
  
      if cast_to_fp32:
          output_tensor = output_tensor.to(dtype=dtype)
  
          # cast back the weights
          self.lora_A[adapter].weight.data = weight_A.to(dtype)
          self.lora_B[adapter].weight.data = weight_B.to(dtype)
  
      return output_tensor
  
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 5 for calling the Conv2d obj's underlying member method code with example usage: obj.get_delta_weight(adapter) and returns: output_tensor