def _initialize(self, y, layer_units):
        # set all attributes, allocate weights etc for first call
        # Initialize parameters
        self.n_iter_ = 0
        self.t_ = 0
        self.n_outputs_ = y.shape[1]

        # Compute the number of layers
        self.n_layers_ = len(layer_units)

        # Output for regression
        if not isinstance(self, ClassifierMixin):
            self.out_activation_ = 'identity'
        # Output for multi class
        elif self.label_binarizer_.y_type_ == 'multiclass':
            self.out_activation_ = 'softmax'
        # Output for binary class and multi-label
        else:
            self.out_activation_ = 'logistic'
            if self.loss == 'log_loss':
                self.loss = 'binary_log_loss'

        # Initialize coefficient and intercept layers
        self.coefs_ = []
        self.intercepts_ = []

        for i in range(self.n_layers_ - 1):
            rng = check_random_state(self.random_state)
            coef_init, intercept_init = self._init_coef(layer_units[i],
                                                        layer_units[i + 1],
                                                        rng)
            self.coefs_.append(coef_init)
            self.intercepts_.append(intercept_init)

        if _STOCHASTIC_ALGOS in self.algorithm:
            self.loss_curve_ = []
            self._no_improvement_count = 0
            if self.early_stopping:
                self.validation_scores_ = []
                self.best_validation_score_ = -np.inf
            else:
                self.best_loss_ = np.inf
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/neural_network/multilayer_perceptron.py/BaseMultilayerPerceptron._initialize/swapped operands of `in`