code: def softmax(x):
      row_wise_max = np.max(x, axis=1).reshape(-1, 1)
      exp_x = np.exp(x - row_wise_max)
      return exp_x / np.sum(exp_x, axis=1).reshape(-1, 1)
  
imports: import pickle
  from functools import lru_cache
  from inspect import getfullargspec
  import cloudpickle
  import joblib
  import numpy
  import sklearn.datasets
  from sklearn.utils import check_random_state
  import lightgbm
label_desc: Use image node_id 9 for calling a global function with example usage: softmax(x) and returns: unknown