code: def op_min(x, nbs):
      res = np.zeros_like(x)
      for u in range(len(nbs)):
          nb = nbs[u]
          if len(nb != 0):
              res[u] = np.min(x[nb], axis=0)
      return res
  
imports: from sklearn import preprocessing
  import numpy
  from sklearn.metrics.pairwise import cosine_similarity
  import copy
  from tqdm import tqdm
  from tabulate import tabulate
  import time
  from .base import BaseFeature, BaseFeatureEngineer
  from .selectors import SeGBDT
  from .None import register_feature
  from ...utils import get_logger
  import torch
label_desc: Use image node_id 4 for calling a global function with example usage: op_min(x, nbs) and returns: res