code: def forward(
      self, x: torch.Tensor, *args: Any, **kwargs: Any
  ) -> torch.Tensor:
      # TODO: no dtype conversion here, unlike in Linear, is that correct?
      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_embedding_A:
                  continue
              embedding_A = self.lora_embedding_A[active_adapter].T
              embedding_B = self.lora_embedding_B[active_adapter].T
              scaling = self.scaling[active_adapter]
              after_A = self._embed(x, embedding_A)
              result += (after_A @ embedding_B) * scaling
  
      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 7 for calling the Embedding obj's underlying member method code with example usage: obj.forward(x) and returns: result