code: def __init__(self, num_tok, dim, dim_out, mult=2):
      super().__init__()
  
      self.dim_in = dim
      self.dim_out = dim_out
  
      self.net_tok = nn.Sequential(
          nn.Linear(num_tok, int(num_tok * mult)),
          nn.GELU(),
          nn.Linear(int(num_tok * mult), int(num_tok * mult)),
          nn.GELU(),
          nn.Linear(int(num_tok * mult), num_tok),
      )
  
      self.net_sen = nn.Sequential(
          nn.Linear(dim, int(dim * mult)),
          nn.GELU(),
          nn.Linear(int(dim * mult), int(dim * mult)),
          nn.GELU(),
          nn.Linear(int(dim * mult), dim_out),
      )
  
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 1 to create a new TranslatorBaseNoLN object from inherited base classes: nn with example: obj = TranslatorBaseNoLN(num_tok, dim, dim_out, mult)