class_docstr: Class providing the core functionality of a parameter, aka
  value, internal/model parameters, mutation, recombination
  and additional features such as shared random state,
  constraint check, hashes, generation and naming.
  The value field should sent to the function to optimize.
  
  Example
  -------
  >>> ng.p.Array(shape=(2,)).value
  array([0., 0.])
docstr: Get the standardized data representing the value of the instance as an array in the optimization space.
  In this standardized space, a mutation is typically centered and reduced (sigma=1) Gaussian noise.
  The data only represent the value of this instance, not the parameters (eg.: mutable sigma), hence it does not
  fully represent the state of the instance. Also, in stochastic cases, the value can be non-deterministically
  deduced from the data (eg.: categorical variable, for which data includes sampling weights for each value)
  
  Parameters
  ----------
  reference: Parameter
      the reference instance for representation in the standardized data space. This keyword parameter is
      mandatory to make the code clearer.
      If you use "self", this method will always return a zero vector.
  
  Returns
  -------
  np.ndarray
      the representation of the value in the optimization space
  
  Note
  ----
  - Operations between different standardized data should only be performed if each array was produced
    by the same reference in the exact same state (no mutation)
  - to make the code more explicit, the "reference" parameter is enforced as a keyword-only parameter.
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code: def get_standardized_data(self: P, *, reference: P) -> np.ndarray:
      """Get the standardized data representing the value of the instance as an array in the optimization space.
      In this standardized space, a mutation is typically centered and reduced (sigma=1) Gaussian noise.
      The data only represent the value of this instance, not the parameters (eg.: mutable sigma), hence it does not
      fully represent the state of the instance. Also, in stochastic cases, the value can be non-deterministically
      deduced from the data (eg.: categorical variable, for which data includes sampling weights for each value)
  
      Parameters
      ----------
      reference: Parameter
          the reference instance for representation in the standardized data space. This keyword parameter is
          mandatory to make the code clearer.
          If you use "self", this method will always return a zero vector.
  
      Returns
      -------
      np.ndarray
          the representation of the value in the optimization space
  
      Note
      ----
      - Operations between different standardized data should only be performed if each array was produced
        by the same reference in the exact same state (no mutation)
      - to make the code more explicit, the "reference" parameter is enforced as a keyword-only parameter.
      """
      assert reference is None or isinstance(
          reference, self.__class__
      ), f"Expected {type(self)} but got {type(reference)} as reference"
      return self._internal_get_standardized_data(
          self if reference is None else reference
      )
  
imports: import uuid
  import warnings
  import numpy
  import nevergrad.common.typing
  from nevergrad.common import errors
  from .None import utils
  from ._layering import ValueProperty
  from ._layering import Layered
  from ._layering import Level
label_desc: Use image node_id 10 for calling the Parameter obj's underlying member method code with example usage: obj.get_standardized_data() and returns: self