class_docstr: Linear mean.
  
  Parameters
  ----------
  space : Manifold
      Equipped manifold.
  
  Attributes
  ----------
  estimate_ : array-like, shape=[*space.shape]
      If fit, Frechet mean.
class_docstr_tok: ["Linear","mean",".","Parameters","--","--","--","--","--","space",":","Manifold","Equipped","manifold",".","Attributes","--","--","--","--","--","estimate_",":","array-like",",","shape=","[","*","space.shape","]","If","fit",",","Frechet","mean","."]
docstr: Compute the Euclidean mean.
  
  Parameters
  ----------
  X : array-like, shape=[n_samples, *metric.shape]
      Training input samples.
  y : None
      Target values. Ignored.
  weights : array-like, shape=[n_samples,]
      Weights associated to the samples.
      Optional, default: None, in which case it is equally weighted.
  
  Returns
  -------
  self : object
      Returns self.
code: def fit(self, X, y=None, weights=None):
      """Compute the Euclidean mean.
  
      Parameters
      ----------
      X : array-like, shape=[n_samples, *metric.shape]
          Training input samples.
      y : None
          Target values. Ignored.
      weights : array-like, shape=[n_samples,]
          Weights associated to the samples.
          Optional, default: None, in which case it is equally weighted.
  
      Returns
      -------
      self : object
          Returns self.
      """
      self.estimate_ = linear_mean(points=X, weights=weights)
      return self
  
imports: import abc
  import logging
  import math
  from sklearn.base import BaseEstimator
  import geomstats.backend
  import geomstats.errors
  from geomstats.geometry.discrete_curves import ElasticTranslationMetric, SRVTranslationMetric
  from geomstats.geometry.euclidean import EuclideanMetric
  from geomstats.geometry.hypersphere import HypersphereMetric
  from geomstats.geometry.matrices import MatricesMetric
  from geomstats.geometry.minkowski import MinkowskiMetric
label_desc: Use image node_id 2 for calling the LinearMean obj's underlying member method code with example usage: obj.fit(X, y, weights) and returns: self