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)
          if len(result.shape) == 4:
              result = result.permute(0, 2, 3, 1)
  
          base_layer = self.get_base_layer()
          base_bias = base_layer.bias
          if base_bias is not None:
              # Bias should be added after OFT forward
              result = result - base_bias.data
  
          # Execute all the adapters
          for active_adapter in self.active_adapters:
              if active_adapter not in self._available_adapters:
                  continue
  
              module_dropout = self.module_dropout[active_adapter]
  
              # Modify current execution weights
              if (not self.training) or (
                  self.training and torch.rand(1) > module_dropout
              ):
                  result = self._get_delta_activations(
                      active_adapter, result, *args, **kwargs
                  )
  
          if base_bias is not None:
              result = result + base_bias.data
          if len(result.shape) == 4:
              result = result.permute(0, 3, 1, 2)
  
      result = result.to(previous_dtype)
      return result
  
imports: import math
  import warnings
  from typing import Any, List, Optional, Set, Tuple
  import torch
  import torch.nn
  from peft.tuners.lycoris_utils import LycorisLayer
label_desc: Use image node_id 14 for calling the OFTLayer obj's underlying member method code with example usage: obj.forward(x) and returns: result