code: def make_product(
      *functions: Callable, exponents: Optional[Sequence] = None
  ) -> Callable:
      """
      Takes the given functions and makes a function which returns the product of the output of original functions. It
      works well with functions returning numpy arrays of the same shape.
  
      Args:
          *functions: Base functions for the product. The functions shall have the same argument and if they return numpy
              arrays, the returned arrays shall have the same shape.
          exponents: Exponents of the functions in the product. The i-th given function in the product will be raised to
              the power of exponents[i].
  
      Returns:
          A function which returns the product function of the given functions output.
      """
      if exponents is None:
          exponents = np.ones(shape=(len(functions)))
      else:
          assert len(functions) == len(exponents), (
              "the length of exponents must be the "
              "same as the number of given functions"
          )
  
      def product_function(*args, **kwargs):
          return np.prod(
              [
                  functions[i](*args, **kwargs) ** exponents[i]
                  for i in range(len(exponents))
              ],
              axis=0,
          )
  
      return product_function
  
imports: from typing import Callable, Optional, Sequence, Tuple
  import numpy
  from modAL.utils.data import modALinput
  from sklearn.base import BaseEstimator
label_desc: Use image node_id 2 for calling a global function with example usage: make_product() and returns: product_function, np