code: def _make_parametrization(
      name: str,
      dimension: int,
      bounding_method: str = "bouncing",
      rolling: bool = False,
      as_tuple: bool = False,
  ) -> ng.p.Parameter:
      """Creates appropriate parametrization for a Photonics problem
  
      Parameters
      name: str
          problem name, among bragg, chirped, cf_photosic_realistic, cf_photosic_reference and morpho
      dimension: int
          size of the problem among 16, 40 and 60 (morpho) or 80 (bragg and chirped)
      bounding_method: str
          transform type for the bounding ("arctan", "tanh", "bouncing" or "clipping"see `Array.bounded`)
      as_tuple: bool
          whether we should use a Tuple of Array instead of a 2D-array.
  
      Returns
      -------
      Instrumentation
          the parametrization for the problem
      """
      if name == "bragg":
          shape = (2, dimension // 2)
          bounds = [(2, 3), (30, 180)]
      elif name == "cf_photosic_realistic":
          shape = (2, dimension // 2)
          bounds = [(1, 9), (30, 180)]
      elif name == "cf_photosic_reference":
          shape = (1, dimension)
          bounds = [(30, 180)]
      elif name == "chirped":
          shape = (1, dimension)
          bounds = [(30, 180)]
      elif name == "morpho":
          shape = (4, dimension // 4)
          bounds = [(0, 300), (0, 600), (30, 600), (0, 300)]
      else:
          raise NotImplementedError(
              f"Transform for {name} is not implemented"
          )
      divisor = max(2, len(bounds))
      assert (
          not dimension % divisor
      ), f"points length should be a multiple of {divisor}, got {dimension}"
      assert (
          shape[0] * shape[1] == dimension
      ), f"Cannot work with dimension {dimension} for {name}: not divisible by {shape[0]}."
      b_array = np.array(bounds)
      assert (
          b_array.shape[0] == shape[0]
      )  # pylint: disable=unsubscriptable-object
      ones = np.ones((1, int(shape[1])))
      init = np.sum(b_array, axis=1, keepdims=True).dot(ones) / 2  # type: ignore
      if as_tuple:
          instrum = ng.p.Instrumentation(
              *[
                  ng.p.Array(init=init[:, i]).set_bounds(
                      b_array[:, 0],
                      b_array[:, 1],
                      method=bounding_method,
                      full_range_sampling=True,
                  )
                  for i in range(init.shape[1])  # type: ignore
              ]
          ).set_name("as_tuple")
          assert instrum.dimension == dimension, instrum
          return instrum
      array = ng.p.Array(init=init)  # type: ignore
      if bounding_method not in ("arctan", "tanh"):
          # sigma must be adapted for clipping and constraint methods
          sigma = ng.p.Array(init=[[10.0]] if name != "bragg" else [[0.03], [10.0]]).set_mutation(exponent=2.0)  # type: ignore
          array.set_mutation(sigma=sigma)
      if rolling:
          mutation = mutations.MutationChoice(
              [mutations.Cauchy(), mutations.Translation(axis=1)]
          )
          array = mutation(array)
      array.set_bounds(
          b_array[:, [0]],
          b_array[:, [1]],
          method=bounding_method,
          full_range_sampling=True,
      )
      array = ng.ops.mutations.Crossover(axis=1)(array).set_name("")
      assert (
          array.dimension == dimension
      ), f"Unexpected {array} for dimension {dimension}"
      return array
  
file_path: nevergrad/nevergrad/functions/photonics/core.py
imports: import typing
  import numpy
  import nevergrad
  from nevergrad.ops import mutations
  from .None import photonics
  from ..None import base
label_desc: Use image node_id 1 for calling a global function with example usage: _make_parametrization(name, dimension, bounding_method, rolling, as_tuple) and returns: array, instrum