code: def dim10_select_one_feature(
      seed: tp.Optional[int] = None,
  ) -> tp.Iterator[
      Experiment
  ]:  # One and only one variable matters - LHS wins.
      # prepare list of parameters to sweep for independent variables
      seedg = create_seed_generator(seed)
      names = ["sphere"]
      optims = sorted(
          x
          for x, y in optimizers.registry.items()
          if y.one_shot and "arg" not in x and "mal" not in x
      )
      functions = [
          ArtificialFunction(
              name,
              block_dimension=bd,
              num_blocks=n_blocks,
              useless_variables=bd * uv_factor * n_blocks,
          )
          for name in names
          for bd in [1]
          for uv_factor in [10]
          for n_blocks in [1]
      ]
      for func in functions:
          for optim in optims:
              for budget in [8, 10, 12, 14, 16, 18, 20]:
                  yield Experiment(
                      func,
                      optim,
                      budget=budget,
                      num_workers=1,
                      seed=next(seedg),
                  )
  
imports: import typing
  import numpy
  from nevergrad import optimizers
  from nevergrad.optimization.base import ConfiguredOptimizer
  from nevergrad.optimization import experimentalvariants
  from nevergrad.functions import ArtificialFunction
  from .xpbase import registry
  from .xpbase import create_seed_generator
  from .xpbase import Experiment
label_desc: Use image node_id 8 for calling a global function with example usage: dim10_select_one_feature(seed) without return types