class_docstr: Base class for a DS method based on the potential function model.
  All DS methods based on the Potential function should inherit from this
  class.
  
  Warning: This class should not be used directly.
  Use derived classes instead.
docstr: Gaussian potential function to decrease the
  influence of the source of competence as the distance between
  :math:`\mathbf{x}_{k}` and the query :math:`\mathbf{x}_{q}`
  increases. The function is computed using the following equation:
  
  .. math:: potential = exp( -dist (\mathbf{x}_{k},
   \mathbf{x}_{q})^{2} )
  
  where dist represents the Euclidean distance between
  :math:`\mathbf{x}_{k}` and :math:`\mathbf{x}_{q}`
  
  Parameters
  ----------
  dist : array of shape = [self.n_samples]
         distance between the corresponding sample to the query
  
  Returns
  -------
  The result of the potential function for each value in (dist)
code: def potential_func(dist):
      """Gaussian potential function to decrease the
      influence of the source of competence as the distance between
      :math:`\\mathbf{x}_{k}` and the query :math:`\\mathbf{x}_{q}`
      increases. The function is computed using the following equation:
  
      .. math:: potential = exp( -dist (\\mathbf{x}_{k},
       \\mathbf{x}_{q})^{2} )
  
      where dist represents the Euclidean distance between
      :math:`\\mathbf{x}_{k}` and :math:`\\mathbf{x}_{q}`
  
      Parameters
      ----------
      dist : array of shape = [self.n_samples]
             distance between the corresponding sample to the query
  
      Returns
      -------
      The result of the potential function for each value in (dist)
      """
      return np.exp(-(dist**2))
  
file_path: DESlib/deslib/des/probabilistic/base.py
imports: from abc import abstractmethod, ABCMeta
  import numpy
  from deslib.des.base import BaseDES
label_desc: Use image node_id 6 for calling the BaseProbabilistic obj's underlying member method code with example usage: obj.potential_func(dist) and returns: np