code: def __init__(
      self,
      verbose: bool = False,
      device: torch.device = torch.device("cpu"),
  ):
      super().__init__()
      self.verbose = verbose
      self._device = None
      self.clip_model, _ = clip.load("ViT-L/14", device=device)
      self.clip_model.eval()
  
      self.cpu_w_weights, self.cpu_w_biases = load_model_weights(
          os.path.join(RESOURCES_ROOT, "w_head_v1.npz")
      )
      self.cpu_p_weights, self.cpu_p_biases = load_model_weights(
          os.path.join(RESOURCES_ROOT, "p_head_v1.npz")
      )
      self.w_threshold, self.p_threshold = 0.5, 0.5
  
file_path: generative-models/scripts/util/detection/nsfw_and_watermark_dectection.py
imports: import os
  import clip
  import numpy
  import torch
  import torchvision.transforms
  from PIL import Image
label_desc: Use image node_id 1 to create a new DeepFloydDataFiltering object from inherited base classes: object with example: obj = DeepFloydDataFiltering(verbose, device)