code: def forward(self, x: torch.Tensor):
      if self.r > 0 and not self.merged:
          result = nn.Embedding.forward(self, x)
          if self.r > 0:
              after_A = F.embedding(
                  x,
                  self.lora_A.transpose(0, 1),
                  self.padding_idx,
                  self.max_norm,
                  self.norm_type,
                  self.scale_grad_by_freq,
                  self.sparse,
              )
              result += (
                  after_A @ self.lora_B.transpose(0, 1)
              ) * self.scaling
          return result
      else:
          return nn.Embedding.forward(self, x)
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  import math
  from torch.utils.checkpoint import checkpoint
  from typing import Dict
  import os
label_desc: Use image node_id 4 for calling the Embedding obj's underlying member method code with example usage: obj.forward(x) and returns: result, nn