code: def forward(self, input):
      if self.fuse_lora:
          return F.linear(input, self.weight, self.bias)
      else:
          return (
              F.linear(input, self.weight, self.bias)
              + (
                  self.lora_dropout(input)
                  @ self.lora_right_weight
                  @ self.lora_left_weight
              )
              * self.lora_scaling
          )
  
imports: import os
  import math
  import torch
  import torch.nn.functional
  import pytest
  import deepspeed
  from deepspeed.runtime.zero import GatheredParameters
  from deepspeed.ops.op_builder import OpBuilder
  from deepspeed.utils import safe_get_full_grad
  import numpy.testing
  from unit.common import DistributedTest
  from deepspeed.ops.op_builder import InferenceBuilder
  from transformers import AutoConfig, AutoTokenizer, AutoModelForCausalLM
label_desc: Use image node_id 5 for calling the LinearLayer_LoRA obj's underlying member method code with example usage: obj.forward(input) and returns: F, unknown