class_docstr: Class to derive the information metric from the pdf in InformationManifoldMixin.
  
  Given a statistical manifold with coordinates :math:`\theta`,
  the Fisher information metric is:
  
  .. math::
      g_{j k}(\theta)=\int_X \frac{\partial \log p(x, \theta)}
      {\partial \theta_j}\frac{\partial \log p(x, \theta)}
      {\partial \theta_k} p(x, \theta) d x
  
  Attributes
  ----------
  space : InformationManifold
      Riemannian Manifold for a parametric family of (real) distributions.
  support : list, shape = (2,)
      Left and right bounds for the support of the distribution.
      But this is just to help integration, bounds should be as large as needed.
code: def __init__(self, space, support):
      super().__init__(
          space=space,
          signature=(space.dim, 0),
      )
      self.support = support
  
imports: from scipy.integrate import quad_vec
  import geomstats.backend
  from geomstats.geometry.riemannian_metric import RiemannianMetric
label_desc: Use image node_id 1 to create a new FisherRaoMetric object from inherited base classes: RiemannianMetric with example: obj = FisherRaoMetric(space, support)