code: def train(self, mode: bool = True):
      nn.Embedding.train(self, mode)
      if mode:
          if self.merge_weights and self.merged:
              # Make sure that the weights are not merged
              if self.r > 0:
                  self.weight.data -= (
                      self.lora_B @ self.lora_A
                  ).transpose(0, 1) * self.scaling
              self.merged = False
      else:
          if self.merge_weights and not self.merged:
              # Merge the weights and mark it
              if self.r > 0:
                  self.weight.data += (
                      self.lora_B @ self.lora_A
                  ).transpose(0, 1) * self.scaling
              self.merged = True
  
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 3 for calling the Embedding obj's underlying member method code with example usage: obj.train(mode) without return types