code: def _hp_parametrization_to_dict(x, **kwargs):
      if isinstance(x, p.Instrumentation):
          x_dict = kwargs.get("default", {})
          for idx_param in range(len(x[0].value)):
              x_dict.update(
                  _hp_parametrization_to_dict(
                      x[0][idx_param], name=str(idx_param)
                  )
              )
          for name in x[1].value.keys():
              x_dict.update(
                  _hp_parametrization_to_dict(x[1][name], name=name)
              )
          return x_dict
      elif isinstance(x, (p.Log, p.Scalar)):
          return {kwargs["name"]: [x.value]}
      elif isinstance(x, p.Choice):
          x_dict = {}
          for i in range(len(x.choices)):
              if x.value == x.choices[i].value:
                  x_dict[kwargs["name"]] = [i]
                  if isinstance(
                      x.choices[i], (p.Log, p.Scalar, p.Choice)
                  ):
                      x_dict[kwargs["name"] + f"__{i}"] = [
                          x.choices[i].value
                      ]
                  elif isinstance(x.choices[i], p.Instrumentation):
                      x_dict.update(
                          _hp_parametrization_to_dict(x.choices[i])
                      )
          return x_dict
  
imports: import numpy
  from .None import base
  from .base import IntOrParameter
  import nevergrad.common.typing
  from nevergrad.parametrization import transforms
  from nevergrad.parametrization import parameter
  import hyperopt
  from hyperopt import hp, Trials, Domain, tpe
label_desc: Use image node_id 1 for calling a global function with example usage: _hp_parametrization_to_dict(x) and returns: x_dict, dict, x_dict