'Fit the model to data matrix X and target y.
Parameters
X : {array-like, sparse matrix}, shape (n_samples, n_features)
The input data.
y : array-like, shape (n_samples,)
The target values.
classes : array, shape (n_classes)
Classes across all calls to partial_fit.
Can be obtained via `np.unique(y_all)`, where y_all is the
target vector of the entire dataset.
This argument is required for the first call to partial_fit
and can be omitted in the subsequent calls.
Note that y doesn\'t need to contain all labels in `classes`.
Returns
self : returns a trained MLP model.'

@property
def partial_fit(self):

    if (self.solver not in _STOCHASTIC_SOLVERS):
        raise AttributeError(('partial_fit    is    only    available    for    stochastic    optimizer.    %s    is    not    stochastic' % self.solver))
    return self._partial_fit
