class_docstr: Base class for gradient descent.
  
  Parameters
  ----------
  max_iter : int, optional
      Maximum number of iterations for the gradient descent.
  epsilon : float, optional
      Tolerance for stopping the gradient descent.
  init_point : array-like, shape=[*metric.shape]
      Initial point.
      Optional, default : None. In this case the first sample of the input
      data is used.
  init_step_size : float
      Learning rate in the gradient descent.
      Optional, default: 1.
  verbose : bool
      Level of verbosity to inform about convergence.
      Optional, default: False.
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docstr: Perform gradient descent.
code: def minimize(self, space, points, weights=None):
      """Perform gradient descent."""
      pass
  
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 BaseGradientDescent obj's underlying member method code with example usage: obj.minimize(space, points, weights) without return types