class Foo (BaseEstimator):
    """C-Support Vector Classification.

    Parameters
    ----------

    C : float, optional (default=1.0)
        penalty parameter C of the error term.
    
    kernel : string, optional
         Description of this members.

    Attributes
    ----------

    `bar_` : array-like, shape = [n_features]
        Brief description of this attribute.



    Examples
    --------
    >>> clf = Foo()
    >>> clf.fit()
    []

    See also
    --------
    OtherClass
    """

  def fit(self, X, Y):
      """Fit the SVM model to the given training data and parameters.

      Give additional details on how the algorithm works (e.g. the objective
      function) and some element to understand the space and time complexity.

      Parameters
      ----------
      X : array-like, shape = [n_samples, n_features]
          Training vector, where n_samples in the number of samples and
          n_features is the number of features.
      Y : array, shape = [n_samples]
          Target values (integers in classification, real numbers in
          regression)
      
      Returns
      --------
      self
      """

  def predict(self, X):
      """Predict class membership index for each input sample.

      This function does classification on an array of
      test vectors X.


      Parameters
      ----------
      X : array-like, shape = [n_samples, n_features]


      Returns
      -------
      C : array, shape = [n_samples]
      """