message: fix minimal example
diff: @@ -604,25 +604,11 @@ class Atol(BaseEstimator, TransformerMixin):
  This class allows to vectorise measures (e.g. point clouds, persistence diagrams, etc) after a quantisation step.
  ATOL paper: https://arxiv.org/abs/1909.13472
  - """
  - def __init__(self, quantiser, weighting_method="cloud", contrast="gaus"):
  - """
  - Constructor for the Atol measure vectorisation class.
  -
  - Parameters:
  - quantiser (Object): Object with `fit` (sklearn API consistent) and `cluster_centers` and `n_clusters`
  - attributes. This object will be fitted by the function `fit`.
  - weighting_method (string): constant generic function for weighting the measure points
  - choose from {"cloud", "iidproba"}
  - (default: constant function, i.e. the measure is seen as a point cloud by default).
  - This will have no impact if weights are provided along with measures all the way: `fit` and `transform`.
  - contrast (string): constant function for evaluating proximity of a measure with respect to centers
  - choose from {"gaussian", "laplacian", "indicator"}
  - (default: laplacian contrast function, see page 3 in the ATOL paper).
  Example
  --------
  >>> from sklearn.cluster import KMeans
  + >>> from gudhi.representations.vector_methods import Atol
  >>> import numpy as np
  >>> a = np.array([[1, 2, 4], [1, 4, 0], [1, 0, 4]])
  >>> b = np.array([[4, 2, 0], [4, 4, 0], [4, 0, 2]])
  @@ -641,6 +627,21 @@ class Atol(BaseEstimator, TransformerMixin):
  [1.04696684, 0.56203292],
  [1.02816136, 0.23559623]])
  """
  + def __init__(self, quantiser, weighting_method="cloud", contrast="gaus"):
  + """
  + Constructor for the Atol measure vectorisation class.
  +
  + Parameters:
  + quantiser (Object): Object with `fit` (sklearn API consistent) and `cluster_centers` and `n_clusters`
  + attributes. This object will be fitted by the function `fit`.
  + weighting_method (string): constant generic function for weighting the measure points
  + choose from {"cloud", "iidproba"}
  + (default: constant function, i.e. the measure is seen as a point cloud by default).
  + This will have no impact if weights are provided along with measures all the way: `fit` and `transform`.
  + contrast (string): constant function for evaluating proximity of a measure with respect to centers
  + choose from {"gaussian", "laplacian", "indicator"}
  + (default: laplacian contrast function, see page 3 in the ATOL paper).
  + """
  self.quantiser = quantiser
  self.contrast = {
  "gaussian": _gaus_contrast,
  