class_docstr: Class for the manifold of Gamma distributions.
  
  This is :math:`Gamma = (R_+^*)^2`, the positive quadrant of the
  2-dimensional Euclidean space.
class_docstr_tok: ["Class","for","the","manifold","of","Gamma","distributions",".","This","is",":","math",":","`","Gamma","=","(","R_+^","*",")","^2","`",",","the","positive","quadrant","of","the","2-dimensional","Euclidean","space","."]
docstr: Metric to equip the space with if equip is True.
docstr_tok: ["Metric","to","equip","the","space","with","if","equip","is","True","."]
code: def default_metric():
      """Metric to equip the space with if equip is True."""
      return GammaMetric
  
imports: from scipy.stats import gamma
  import geomstats.backend
  from geomstats.algebra_utils import from_vector_to_diagonal_matrix
  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
  from geomstats.numerics.bvp import ScipySolveBVP
  from geomstats.numerics.geodesic import ExpODESolver, LogODESolver
  from geomstats.numerics.ivp import ScipySolveIVP
  from geomstats.vectorization import check_is_batch
label_desc: Use image node_id 2 for calling the GammaDistributions obj's underlying member method code with example usage: obj.default_metric() and returns: GammaMetric