class_docstr: Instrumented function which plays the agent using an environment runner
class_docstr_tok: ["Instrumented","function","which","plays","the","agent","using","an","environment","runner"]
docstr: Implements the call of the function.
  Under the hood, __call__ delegates to oracle_call + add some noise if noise_level > 0.
docstr_tok: ["Implements","the","call","of","the","function",".","Under","the","hood",",","__call__","delegates","to","oracle_call","+","add","some","noise","if","noise_level",">","0","."]
code: def evaluation_function(self, *recommendations: p.Parameter) -> float:
      """Implements the call of the function.
      Under the hood, __call__ delegates to oracle_call + add some noise if noise_level > 0.
      """
      assert (
          len(recommendations) == 1
      ), "Should not be a pareto set for a singleobjective function"
      num_tests = max(
          1,
          int(self._num_test_evaluations / self.runner.num_repetitions),
      )
      return (
          sum(
              self.compute(**recommendations[0].kwargs)
              for _ in range(num_tests)
          )
          / num_tests
      )
  
imports: import warnings
  import operator
  import copy
  import typing
  import gym
  import numpy
  from nevergrad.common.tools import pytorch_import_fix
  from nevergrad.parametrization import parameter
  from ..base import ExperimentFunction
  from .None import base
  from .None import envs
  import torch
  import torch.nn.functional
  from torch import nn
  from torch.utils.data import WeightedRandomSampler
label_desc: Use image node_id 3 for calling the TorchAgentFunction obj's underlying member method code with example usage: obj.evaluation_function() and returns: unknown