class_docstr: Wrapped Gaussian Process.
  
  The implementation is based on the algorithm 4 of [1]_.
  
  Parameters
  ----------
  space : Manifold
      Equipped manifold.
  prior : callable
      Associate to each input a manifold valued point.
  
  References
  ----------
  .. [1] Mallasto, A. and Feragen, A. Wrapped gaussian process
      regression on riemannian manifolds. In 2018 IEEE/CVF
      Conference on Computer Vision and Pattern Recognition
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docstr: Set euclidean_gpr parameters.
  
  Especially useful for one line instantiations.
docstr_tok: ["Set","euclidean_gpr","parameters",".","Especially","useful","for","one","line","instantiations","."]
code: def set(self, **kwargs):
      """Set euclidean_gpr parameters.
  
      Especially useful for one line instantiations.
      """
      for param_name, value in kwargs.items():
          if not hasattr(self.euclidean_gpr, param_name):
              raise ValueError(f"Unknown parameter {param_name}.")
  
          setattr(self.euclidean_gpr, param_name, value)
      return self
  
file_path: geomstats/geomstats/learning/wrapped_gaussian_process.py
imports: from sklearn.base import BaseEstimator, MultiOutputMixin, RegressorMixin
  from sklearn.gaussian_process import GaussianProcessRegressor
  import geomstats.backend
label_desc: Use image node_id 2 for calling the WrappedGaussianProcess obj's underlying member method code with example usage: obj.set() and returns: self