class_docstr: Combines a function and its parametrization for running experiments (see benchmark subpackage).
  Extends ExperimentFunction, in the special case of an array, by allowing the creation of symmetries
  of a single function. We can create ArrayExperimentFunction(callable, symmetry=i) for i in range(0, 2**d)
  when the callable works on R^d.
  Works only if there are no constraints.
  
  Parameters
  ----------
  function: callable
      the callable to convert
  parametrization: Parameter
      the parametrization of the function
  symmetry: int
      number parametrizing how we symmetrize the function.
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code: def symmetrized_function(self, x: np.ndarray) -> tp.Loss:
      assert isinstance(
          x, np.ndarray
      ), "symmetry != 0 works only when the input is an array."
      assert len(x.shape) == 1, "only one-dimensional arrays for now."
      return self._inner_function(
          self.threshold_coefficients + self.slope_coefficients * x
      )
  
imports: import inspect
  from pathlib import Path
  import numbers
  import numpy
  import nevergrad.common.typing
  from nevergrad.common import errors
  from nevergrad.common.errors import UnsupportedExperiment
  from nevergrad.parametrization import parameter
  from nevergrad.optimization import multiobjective
label_desc: Use image node_id 2 for calling the ArrayExperimentFunction obj's underlying member method code with example usage: obj.symmetrized_function(x) and returns: self