def _decision_scores(self, X):
        """Predict using the trained model

        Parameters
        ----------
        X : {array-like, sparse matrix}, shape (n_samples, n_features)
            The input data.

        Returns
        -------
        y_pred : array-like, shape (n_samples,) or (n_samples, n_outputs)
            The decision function of the samples for each class in the model.
        """
        X = check_array(X, accept_sparse=['csr', 'csc', 'coo'])

        # Make sure self.hidden_layer_sizes is a list
        hidden_layer_sizes = self.hidden_layer_sizes
        if not hasattr(hidden_layer_sizes, "__iter__"):
            hidden_layer_sizes = [hidden_layer_sizes]
        hidden_layer_sizes = list(hidden_layer_sizes)

        layer_units = [X.shape[1]] + hidden_layer_sizes + \
            [self.n_outputs_]

        # Initialize layers
        activations = [X]

        for i in range(1 - self.n_layers_):
            activations.append(np.empty((X.shape[0],
                                         layer_units[i + 1])))
        # forward propagate
        self._forward_pass(activations, with_output_activation=False)
        y_pred = activations[-1]

        return y_pred
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/neural_network/multilayer_perceptron.py/BaseMultilayerPerceptron._decision_scores/swapped operands of `-`