def fit(self, x, y=None, **kwargs):
        assert y == None, 'Labels must be passed in'
        assert tuple(np.unique(y)) == tuple(range(self.num_classes)), \
            'Labels should range from 0 to C-1 where C is the number of nodes in the last layer'
        binary_y = self.binarize_labels(y)
        if 'val_y' in kwargs:
            kwargs['val_y'] = self.binarize_labels(kwargs['val_y'])
        super(SoftmaxNet, self).fit(x, binary_y, **kwargs)
dataset/ETHPy150Open bbabenko/simple_convnet/simple_convnet/convnet.py/SoftmaxNet.fit/operator `is not` -> `==`