message: update doc in wasserstein.wasserstein to reflect change in default param
diff: @@ -73,8 +73,8 @@ def _perstot_autodiff(X, order, internal_p):
  def _perstot(X, order, internal_p, enable_autodiff):
  '''
  :param X: (n x 2) numpy.array (points of a given diagram).
  - :param order: exponent for Wasserstein. Default value is 2.
  - :param internal_p: Ground metric on the (upper-half) plane (i.e. norm L^p in R^2); Default value is 2 (Euclidean norm).
  + :param order: exponent for Wasserstein.
  + :param internal_p: Ground metric on the (upper-half) plane (i.e. norm L^p in R^2).
  :param enable_autodiff: If X is torch.tensor, tensorflow.Tensor or jax.numpy.ndarray, make the computation
  transparent to automatic differentiation.
  :type enable_autodiff: bool
  @@ -96,9 +96,9 @@ def wasserstein_distance(X, Y, matching=False, order=1., internal_p=np.inf, enab
  :param matching: if True, computes and returns the optimal matching between X and Y, encoded as
  a (n x 2) np.array [...[i,j]...], meaning the i-th point in X is matched to
  the j-th point in Y, with the convention (-1) represents the diagonal.
  - :param order: exponent for Wasserstein; Default value is 2.
  + :param order: exponent for Wasserstein; Default value is 1.
  :param internal_p: Ground metric on the (upper-half) plane (i.e. norm L^p in R^2);
  - Default value is 2 (Euclidean norm).
  + Default value is `np.inf`.
  :param enable_autodiff: If X and Y are torch.tensor, tensorflow.Tensor or jax.numpy.ndarray, make the computation
  transparent to automatic differentiation. This requires the package EagerPy and is currently incompatible
  with `matching=True`.
  