code: def import_model_class_from_model_name_or_path(
      pretrained_model_name_or_path: str,
      revision: str,
      subfolder: str = "text_encoder",
  ):
      text_encoder_config = PretrainedConfig.from_pretrained(
          pretrained_model_name_or_path,
          subfolder=subfolder,
          revision=revision,
      )
      model_class = text_encoder_config.architectures[0]
  
      if model_class == "CLIPTextModel":
          from transformers import CLIPTextModel
  
          return CLIPTextModel
      elif model_class == "CLIPTextModelWithProjection":
          from transformers import CLIPTextModelWithProjection
  
          return CLIPTextModelWithProjection
      elif model_class == "RobertaSeriesModelWithTransformation":
          from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
              RobertaSeriesModelWithTransformation,
          )
  
          return RobertaSeriesModelWithTransformation
      elif model_class == "T5EncoderModel":
          from transformers import T5EncoderModel
  
          return T5EncoderModel
      else:
          raise ValueError(f"{model_class} is not supported.")
  
imports: import hashlib
  import itertools
  import os
  from pathlib import Path
  from typing import Dict
  import torch
  from diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, StableDiffusionXLPipeline, UNet2DConditionModel
  from diffusers.utils.import_utils import is_xformers_available
  from packaging import version
  from tqdm import tqdm
  from transformers import AutoTokenizer, PretrainedConfig
  from autotrain import logger
  from autotrain.trainers.dreambooth.datasets import PromptDataset
label_desc: Use image node_id 2 for calling a global function with example usage: import_model_class_from_model_name_or_path(pretrained_model_name_or_path, revision, subfolder) and returns: CLIPTextModel, CLIPTextModelWithProjection, RobertaSeriesModelWithTransformation, T5EncoderModel