class_docstr: Empirical exponential barycenter for matrix groups.
  
  Parameters
  ----------
  space : LieGroup
      Lie group instance on which the data lie.
  
  Attributes
  ----------
  estimate_ : array-like, shape=[dim, dim]
      If fit, exponential barycenter.
docstr: Set optimizer parameters.
  
  Especially useful for one line instantiations.
docstr_tok: ["Set","optimizer","parameters",".","Especially","useful","for","one","line","instantiations","."]
code: def set(self, **kwargs):
      """Set optimizer parameters.
  
      Especially useful for one line instantiations.
      """
      for param_name, value in kwargs.items():
          if not hasattr(self.optimizer, param_name):
              raise ValueError(f"Unknown parameter {param_name}.")
  
          setattr(self.optimizer, param_name, value)
      return self
  
file_path: geomstats/geomstats/learning/exponential_barycenter.py
imports: import logging
  from sklearn.base import BaseEstimator
  import geomstats.backend
  from geomstats.geometry.euclidean import Euclidean
  from geomstats.learning.frechet_mean import BaseGradientDescent, LinearMean
label_desc: Use image node_id 3 for calling the ExponentialBarycenter obj's underlying member method code with example usage: obj.set() and returns: self