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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code: def __init__(self, space, prior):
      self.space = space
      self.prior = prior
  
      self.euclidean_gpr = GaussianProcessRegressor(
          kernel=None,
          alpha=1e-10,
          optimizer="fmin_l_bfgs_b",
          n_restarts_optimizer=0,
          normalize_y=False,
          copy_X_train=True,
          random_state=None,
      )
  
      self.tangent_y_train_ = None
  
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 1 to create a new WrappedGaussianProcess object from inherited base classes: MultiOutputMixin, RegressorMixin, BaseEstimator with example: obj = WrappedGaussianProcess(space, prior)