class_docstr: Parameters
  ----------
  nint intaum_stocks: number of stocks to be managed
  depth: number of layers in the neural networks
  width: number of neurons per hidden layer
class_docstr_tok: ["Parameters","--","--","--","--","--","nint","intaum_stocks",":","number","of","stocks","to","be","managed","depth",":","number","of","layers","in","the","neural","networks","width",":","number","of","neurons","per","hidden","layer"]
code: def evaluation_function(self, *recommendations: p.Parameter) -> float:
      assert (
          len(recommendations) == 1
      ), "Should not be a pareto set for a singleobjective function"
      x = recommendations[0].value
      # pylint: disable=not-callable
      loss = sum([self.function(x) for _ in range(42)]) / 42.0
      assert isinstance(loss, float)
      return loss
  
imports: import functools
  import numpy
  from nevergrad.functions import ExperimentFunction
  from nevergrad.parametrization import parameter
label_desc: Use image node_id 3 for calling the Game obj's underlying member method code with example usage: obj.evaluation_function() and returns: loss