code: def generate_trials(space, r_factor=3):
      """Generates a series of trials.
  
      This algorithm generates a series of non-deterministic trials given a
      space of options to test. A trial is generated by pulling a value from
      each option in the space. On some occasions the values are shuffled to
      ensure a different trial on each r_factor iteration. The algorithm ensures
      that each value from an option is used at least once. The total number of
      trials is determined by the r_factor * the option with the largest number
      of values.
  
      Parameters
      ----------
      space: List[List[Any]]
          A list of different options with varying values to test.
      r_factor: Optional[int]
          The repeat factor.
  
      Returns
      -------
      result: List[Tuple]
          A list of trials specifying values for each option.
  
      """
      np.random.seed(0)
      max_len = 1
      for option in space:
          max_len = max(max_len, len(option))
  
      num_trials = r_factor * max_len
      trials = []
      for i in range(num_trials):
          trial = []
          for option in space:
              if i % len(option) == 0:
                  np.random.shuffle(option)
              trial.append(option[i % len(option)])
  
          trials.append(trial)
  
      return trials
  
imports: from itertools import zip_longest, combinations
  import json
  import os
  import warnings
  import numpy
  import tvm
  from tvm import relay
  from tvm import rpc
  from tvm.contrib import graph_executor
  from tvm.relay.op.contrib.bnns import partition_for_bnns
  from tvm.contrib import utils
  from tvm.autotvm.measure import request_remote
  from tvm.relay.analysis import analysis
label_desc: Use image node_id 10 for calling a global function with example usage: generate_trials(space, r_factor) and returns: trials