code: def softmax(w: list) -> np.ndarray:
      """
      Softmax function
      Args:
          w (list): probabilities
      Returns:
          result (numpy.ndarray): softmax of inputs
      """
      e = np.exp(np.array(w, dtype=np.float32))
      dist = e / np.sum(e)
      return dist
  
imports: import pathlib
  import os
  import sys
  from os import path
  from types import ModuleType
  import multiprocessing
  from typing import Optional, Union
  import numpy
  import pandas
  from chefboost import Chefboost
  from chefboost.commons.logger import Logger
  from chefboost.commons.module import load_module
label_desc: Use image node_id 3 for calling a global function with example usage: softmax(w) and returns: dist