class_docstr: Class for the Fisher information metric on Dirichlet distributions.
class_docstr_tok: ["Class","for","the","Fisher","information","metric","on","Dirichlet","distributions","."]
code: def __init__(self, space):
      super().__init__(space=space)
  
      self.log_solver = LogODESolver(
          n_nodes=1000, integrator=ScipySolveBVP(max_nodes=1000)
      )
      self.exp_solver = ExpODESolver(
          integrator=ScipySolveIVP(method="LSODA")
      )
  
imports: import math
  import numpy
  from scipy.optimize import minimize
  from scipy.stats import dirichlet
  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, ScipyMultivariateRandomVariable
  from geomstats.numerics.bvp import ScipySolveBVP
  from geomstats.numerics.geodesic import ExpODESolver, LogODESolver
  from geomstats.numerics.ivp import ScipySolveIVP
  from geomstats.vectorization import repeat_out
label_desc: Use image node_id 1 to create a new DirichletMetric object from inherited base classes: RiemannianMetric with example: obj = DirichletMetric(space)