class_docstr: Class for the Fisher information metric on binomial distributions.
  
  References
  ----------
  .. [AM1981] Atkinson, C., & Mitchell, A. F. (1981). Rao's distance measure.
      Sankhyā: The Indian Journal of Statistics, Series A, 345-365.
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docstr: Compute exp map of a base point in tangent vector direction.
  
  Parameters
  ----------
  tangent_vec : array-like, shape=[..., 1]
      Tangent vector at base point.
  base_point : array-like, shape=[..., 1]
      Base point.
  
  Returns
  -------
  exp : array-like, shape=[..., 1]
      End point of the geodesic starting at base_point with
      initial velocity tangent_vec.
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code: def exp(self, tangent_vec, base_point):
      """Compute exp map of a base point in tangent vector direction.
  
      Parameters
      ----------
      tangent_vec : array-like, shape=[..., 1]
          Tangent vector at base point.
      base_point : array-like, shape=[..., 1]
          Base point.
  
      Returns
      -------
      exp : array-like, shape=[..., 1]
          End point of the geodesic starting at base_point with
          initial velocity tangent_vec.
      """
      return (
          gs.sin(
              tangent_vec
              / (2.0 * gs.sqrt(base_point) * gs.sqrt(1 - base_point))
              + gs.arcsin(gs.sqrt(base_point))
          )
          ** 2
      )
  
imports: from scipy.special import factorial
  from scipy.stats import binom
  import geomstats.backend
  from geomstats.geometry.base import VectorSpaceOpenSet
  from geomstats.geometry.euclidean import Euclidean
  from geomstats.geometry.riemannian_metric import RiemannianMetric
  from geomstats.information_geometry.base import InformationManifoldMixin, ScipyUnivariateRandomVariable
label_desc: Use image node_id 6 for calling the BinomialMetric obj's underlying member method code with example usage: obj.exp(tangent_vec, base_point) and returns: unknown