class_docstr: A gamma random variable.
class_docstr_tok: ["A","gamma","random","variable","."]
code: def _flatten_params(point, pre_flat_shape):
      param_a = gs.expand_dims(point[..., 0], axis=-1)
      scale = gs.expand_dims(point[..., 1] / point[..., 0], axis=-1)
  
      flat_param_a = gs.reshape(
          gs.broadcast_to(param_a, pre_flat_shape), (-1,)
      )
      flat_scale = gs.reshape(
          gs.broadcast_to(scale, pre_flat_shape), (-1,)
      )
      return {"a": flat_param_a, "scale": flat_scale}
  
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 GammaDistributionsRandomVariable obj's underlying member method code with example usage: obj._flatten_params(point, pre_flat_shape) and returns: dict