code: def __init__(self, num_tok, dim, dim_out, mult=2, depth=5):
      super().__init__()
  
      self.blocks = nn.ModuleList(
          [
              TranslatorBase(num_tok, dim, dim, mult=2)
              for d in range(depth)
          ]
      )
      self.gelu = nn.GELU()
  
      self.tail = TranslatorBaseNoLN(num_tok, dim, dim_out, mult=2)
  
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 TranslatorNoLN object from inherited base classes: nn with example: obj = TranslatorNoLN(num_tok, dim, dim_out, mult, depth)