code: def forward(self, x):
      if self.dim_in == self.dim_out:
          indentity_0 = x
          x = self.net_sen(x)
          x += indentity_0
          x = x.transpose(1, 2)
  
          indentity_1 = x
          x = self.net_tok(x)
          x += indentity_1
          x = x.transpose(1, 2)
      else:
          x = self.net_sen(x)
          x = x.transpose(1, 2)
  
          x = self.net_tok(x)
          x = x.transpose(1, 2)
      return x
  
imports: import inspect
  from typing import Any, Dict, List, Optional, Union
  import torch
  import torch.nn
  from transformers import AutoModel, AutoTokenizer, CLIPImageProcessor
  from diffusers import DiffusionPipeline
  from diffusers.image_processor import VaeImageProcessor
  from diffusers.loaders import LoraLoaderMixin
  from diffusers.models import AutoencoderKL, UNet2DConditionModel
  from diffusers.models.lora import adjust_lora_scale_text_encoder
  from diffusers.pipelines.stable_diffusion.pipeline_output import StableDiffusionPipelineOutput
  from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
  from diffusers.schedulers import KarrasDiffusionSchedulers
  from diffusers.utils import USE_PEFT_BACKEND, logging, scale_lora_layers, unscale_lora_layers
  from diffusers.utils.torch_utils import randn_tensor
label_desc: Use image node_id 2 for calling the TranslatorBaseNoLN obj's underlying member method code with example usage: obj.forward(x) and returns: x