class_docstr: Class for the Fisher information metric on Poisson 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 the metric matrix at the tangent space at base_point.
  
  Parameters
  ----------
  base_point : array-like, shape=[..., 1]
      Point representing a Poisson distribution.
  
  Returns
  -------
  mat : array-like, shape=[..., 1, 1]
      Metric matrix.
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code: def metric_matrix(self, base_point):
      """Compute the metric matrix at the tangent space at base_point.
  
      Parameters
      ----------
      base_point : array-like, shape=[..., 1]
          Point representing a Poisson distribution.
  
      Returns
      -------
      mat : array-like, shape=[..., 1, 1]
          Metric matrix.
      """
      return gs.expand_dims(1 / base_point, axis=-1)
  
imports: from scipy.special import factorial
  from scipy.stats import poisson
  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 2 for calling the PoissonMetric obj's underlying member method code with example usage: obj.metric_matrix(base_point) and returns: gs