code: def test_base_optimizer() -> None:
      zeroptim = xpvariants.Zero(
          parametrization=2, budget=4, num_workers=1
      )
      zeroptim.parametrization.function.deterministic = False
      assert not zeroptim.parametrization.function.deterministic
      representation = repr(zeroptim)
      expected = "parametrization=Array{(2,)}"
      assert (
          expected in representation
      ), f"Unexpected representation: {representation}"
      np.testing.assert_equal(zeroptim.ask().value, [0, 0])
      zeroptim.tell(
          zeroptim.parametrization.spawn_child().set_standardized_data(
              [0.0, 0]
          ),
          0,
      )
      zeroptim.tell(
          zeroptim.parametrization.spawn_child().set_standardized_data(
              [1.0, 1]
          ),
          1,
      )
      np.testing.assert_equal(
          zeroptim.provide_recommendation().value, [0, 0]
      )
      # check that the best value is updated if a second evaluation is not as good
      zeroptim.tell(
          zeroptim.parametrization.spawn_child().set_standardized_data(
              [0.0, 0]
          ),
          10,
      )
      zeroptim.tell(
          zeroptim.parametrization.spawn_child().set_standardized_data(
              [1.0, 1]
          ),
          1,
      )
      np.testing.assert_equal(
          zeroptim.provide_recommendation().value, [1, 1]
      )
      np.testing.assert_equal(zeroptim._num_ask, 1)
      # check suggest
      zeroptim.suggest([12, 12])
      np.testing.assert_array_equal(zeroptim.ask().args[0], [12, 12])
      np.testing.assert_array_equal(zeroptim.ask().args[0], [0, 0])
  
imports: import warnings
  from pathlib import Path
  import numpy
  import nevergrad.common.typing
  from nevergrad.common import testing
  import nevergrad
  from .None import optimizerlib
  from .None import experimentalvariants
  from .None import base
  from .None import utils
  from .None import callbacks
label_desc: Use image node_id 3 for calling a global function with example usage: test_base_optimizer() without return types