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: Possibly multiobjective losses which were told
  to the optimizer along this parameter.
  In case of mono-objective loss, losses is the array containing this loss as sole element
  
  Note
  ----
  This API is highly experimental
docstr_tok: ["Possibly","multiobjective","losses","which","were","told","to","the","optimizer","along","this","parameter",".","In","case","of","mono-objective","loss",",","losses","is","the","array","containing","this","loss","as","sole","element","Note","--","--","This","API","is","highly","experimental"]
code: def losses(self) -> np.ndarray:
      """Possibly multiobjective losses which were told
      to the optimizer along this parameter.
      In case of mono-objective loss, losses is the array containing this loss as sole element
  
      Note
      ----
      This API is highly experimental
      """
      if self._losses is not None:
          return self._losses
      if self.loss is not None:
          return np.array([self.loss], dtype=float)
      raise RuntimeError("No loss was provided")
  
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 2 for calling the Parameter obj's underlying member method code with example usage: obj.losses() and returns: self, np