code: def euclidean_dissim(a, b, **_):
      """Euclidean distance dissimilarity function"""
      if np.isnan(a).any() or np.isnan(b).any():
          raise ValueError(
              "Missing values detected in numerical columns."
          )
      return np.sum((a - b) ** 2, axis=1)
  
imports: import numpy
label_desc: Use image node_id 4 for calling a global function with example usage: euclidean_dissim(a, b) and returns: np