code: def load_model(
      config: str,
      device: str,
      num_frames: int,
      num_steps: int,
  ):
      config = OmegaConf.load(config)
      if device == "cuda":
          config.model.params.conditioner_config.params.emb_models[
              0
          ].params.open_clip_embedding_config.params.init_device = (
              device
          )
  
      config.model.params.sampler_config.params.num_steps = num_steps
      config.model.params.sampler_config.params.guider_config.params.num_frames = (
          num_frames
      )
      if device == "cuda":
          with torch.device(device):
              model = (
                  instantiate_from_config(config.model)
                  .to(device)
                  .eval()
              )
      else:
          model = (
              instantiate_from_config(config.model).to(device).eval()
          )
  
      filter = DeepFloydDataFiltering(verbose=False, device=device)
      return model, filter
  
imports: import math
  import os
  from glob import glob
  from pathlib import Path
  from typing import Optional
  import cv2
  import numpy
  import torch
  from einops import rearrange, repeat
  from fire import Fire
  from omegaconf import OmegaConf
  from PIL import Image
  from torchvision.transforms import ToTensor
  from scripts.util.detection.nsfw_and_watermark_dectection import DeepFloydDataFiltering
  from sgm.inference.helpers import embed_watermark
  from sgm.util import default, instantiate_from_config
label_desc: Use image node_id 4 for calling a global function with example usage: load_model(config, device, num_frames, num_steps) and returns: model,  filter