code: def sample_reducer(limit, max_value, random_method):
      """Sample Reducer (Helper)
  
      NOTE: The Scan() object  is in self.main_self because
      the object being passed here is ParamGrid() object where
      the Scan() object is attached as self.main_self.
  
      Utilize 'grid_downsample', 'shuffle', and 'random_method'
      to reduce the param_grid before starting the experiment.
      This is the simplest method in Talos for dealing with curse
      of dimensionality.
  
      Options are uniform random, stratified random, latin hypercube
      sampling, and latin hypercube with sudoku style constraint.
  
      Returns the reduced param_grid as numpy array.
  
      """
  
      import chances as ch
  
      # calculate the size of the downsample
      if isinstance(limit, float):
          n = int(max_value * limit)
      if isinstance(limit, int):
          n = limit
  
      max_value = int(max_value)
  
      # throw an error if
      from ..utils.exceptions import TalosDataError
  
      if n < 1:
          raise TalosDataError("Limiters lead to < 1 permutations.")
  
      # Initialize Randomizer()
      r = ch.Randomizer(max_value, n)
  
      # use the user selected method
      if random_method == "sobol":
          out = r.sobol()
      elif random_method == "quantum":
          out = r.quantum()
      elif random_method == "halton":
          out = r.halton()
      elif random_method == "korobov_matrix":
          out = r.korobov_matrix()
      elif random_method == "latin_sudoku":
          out = r.latin_sudoku()
      elif random_method == "latin_matrix":
          out = r.latin_matrix()
      elif random_method == "latin_improved":
          out = r.latin_improved()
      elif random_method == "uniform_mersenne":
          out = r.uniform_mersenne()
      elif random_method == "uniform_crypto":
          out = r.uniform_crypto()
      elif random_method == "ambience":
          out = r.ambience()
      else:
          print(
              "No eligble random_method found. Using uniform_mersenne."
          )
          out = r.uniform_mersenne()
  
      return out
  
label_desc: Use image node_id 1 for calling a global function with example usage: sample_reducer(limit, max_value, random_method) and returns: out