class_docstr: Class for Riemannian and pseudo-Riemannian metrics.
  
  The associated Levi-Civita connection on the tangent bundle.
class_docstr_tok: ["Class","for","Riemannian","and","pseudo-Riemannian","metrics",".","The","associated","Levi-Civita","connection","on","the","tangent","bundle","."]
docstr: Metric matrix at the tangent space at a base point.
  
  Parameters
  ----------
  base_point : array-like, shape=[..., dim]
      Base point.
      Optional, default: None.
  
  Returns
  -------
  mat : array-like, shape=[..., dim, dim]
      Inner-product matrix.
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code: def metric_matrix(self, base_point=None):
      """Metric matrix at the tangent space at a base point.
  
      Parameters
      ----------
      base_point : array-like, shape=[..., dim]
          Base point.
          Optional, default: None.
  
      Returns
      -------
      mat : array-like, shape=[..., dim, dim]
          Inner-product matrix.
      """
      raise NotImplementedError(
          "The computation of the metric matrix is not implemented."
      )
  
imports: from abc import ABC
  import joblib
  import geomstats.backend
  import geomstats.geometry
  from geomstats.geometry.connection import Connection
  from geomstats.vectorization import check_is_batch
label_desc: Use image node_id 4 for calling the RiemannianMetric obj's underlying member method code with example usage: obj.metric_matrix(base_point) without return types