code: def predict_proba(X, weights, biases):
      logits = X @ weights.T + biases
      proba = np.where(
          logits >= 0,
          1 / (1 + np.exp(-logits)),
          np.exp(logits) / (1 + np.exp(logits)),
      )
      return proba.T
  
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 for calling a global function with example usage: predict_proba(X, weights, biases) and returns: proba