class_docstr: Elastic mean.
  
  Parameters
  ----------
  space : Manifold
      Equipped manifold.
  
  Attributes
  ----------
  estimate_ : array-like, shape=[*space.shape]
      If fit, Frechet mean.
class_docstr_tok: ["Elastic","mean",".","Parameters","--","--","--","--","--","space",":","Manifold","Equipped","manifold",".","Attributes","--","--","--","--","--","estimate_",":","array-like",",","shape=","[","*","space.shape","]","If","fit",",","Frechet","mean","."]
docstr: Compute the weighted mean of elastic curves.
  
  SRV: Square Root Velocity.
  
  SRV curves are a special case of Elastic curves.
  
  The computation of the mean goes as follows:
  
  - Transform the curves into their SRVs/F-transform representations,
  - Compute the linear mean of the SRVs/F-transform representations,
  - Inverse-transform the mean in curve space.
  
  Parameters
  ----------
  points : array-like, shape=[n_samples, k_sampling_points, dim]
      Points on the manifold of curves (i.e. curves) to be averaged.
  weights : array-like, shape=[n_samples,]
      Weights associated to the points (i.e. curves).
      Optional, default: None.
  
  Returns
  -------
  mean : array-like, shape=[k_sampling_points, dim]
      Weighted linear mean of the points (i.e. of the curves).
code: def _elastic_mean(self, points, weights=None):
      """Compute the weighted mean of elastic curves.
  
      SRV: Square Root Velocity.
  
      SRV curves are a special case of Elastic curves.
  
      The computation of the mean goes as follows:
  
      - Transform the curves into their SRVs/F-transform representations,
      - Compute the linear mean of the SRVs/F-transform representations,
      - Inverse-transform the mean in curve space.
  
      Parameters
      ----------
      points : array-like, shape=[n_samples, k_sampling_points, dim]
          Points on the manifold of curves (i.e. curves) to be averaged.
      weights : array-like, shape=[n_samples,]
          Weights associated to the points (i.e. curves).
          Optional, default: None.
  
      Returns
      -------
      mean : array-like, shape=[k_sampling_points, dim]
          Weighted linear mean of the points (i.e. of the curves).
      """
      diffeo = self.space.metric.diffeo
      transformed = diffeo.diffeomorphism(points)
      transformed_linear_mean = linear_mean(
          transformed, weights=weights
      )
  
      return diffeo.inverse_diffeomorphism(transformed_linear_mean)
  
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 ElasticMean obj's underlying member method code with example usage: obj._elastic_mean(points, weights) and returns: diffeo