code: def load_language_adapter(
      self,
      model_path: str,
      num_token: int,
      dim: int,
      dim_out: int,
      tensor_norm: torch.FloatTensor,
      mult: int = 2,
      depth: int = 5,
  ):
      device = self._execution_device
      self.tensor_norm = tensor_norm.to(device)
      self.language_adapter = TranslatorNoLN(
          num_tok=num_token,
          dim=dim,
          dim_out=dim_out,
          mult=mult,
          depth=depth,
      ).to(device)
      self.language_adapter.load_state_dict(torch.load(model_path))
  
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 GlueGenStableDiffusionPipeline obj's underlying member method code with example usage: obj.load_language_adapter(model_path, num_token, dim, dim_out, tensor_norm, mult, depth) without return types