class_docstr: Iterate over a set of fully-specified hyperparameter combinations.
  
  Args:
      hp_iter (list): list of fully-specified hyperparameter dicts. 
class_docstr_tok: ["Iterate","over","a","set","of","fully-specified","hyperparameter","combinations",".","Args",":","hp_iter","(","list",")",":","list","of","fully-specified","hyperparameter","dicts","."]
code: def get_suggestion(
      self, parameters, results=None, lower_is_better=True
  ):
      if self.count >= len(self.hp_iter):
          # No more combinations to try.
          return None
      else:
          hp = self.hp_iter[self.count]
          self.count += 1
          return hp
  
imports: import os
  import random
  import numpy
  import logging
  import sherpa
  import pandas
  import sklearn.gaussian_process
  from sherpa.core import Choice, Continuous, Discrete, Ordinal, AlgorithmState
  from sherpa.core import rng
  import sklearn.model_selection
  import collections
label_desc: Use image node_id 2 for calling the Iterate obj's underlying member method code with example usage: obj.get_suggestion(parameters, results, lower_is_better) and returns: None, hp