def fit(self, X, y=None):
        """Fit detector. y is ignored in unsupervised methods.

        Parameters
        ----------
        X : numpy array of shape (n_samples, n_features)
            The input samples.

        y : Ignored
            Not used, present for API consistency by convention.

        Returns
        -------
        self : object
            Fitted estimator.
        """
        # validate inputs X and y (optional)
        X = check_array(X)
        self._set_n_classes(y)

        self.mean, self.std = np.mean(X_train, axis=0), np.mean(X_train,
                                                                axis=0)

        train_set = PyODDataset(X=X_train, mean=self.mean, std=self.std)
        train_loader = torch.utils.data.DataLoader(train_set,
                                                   batch_size=batch_size,
                                                   shuffle=True)

        # initialize the model
        self.model = inner_autoencoder(
            hidden_neurons=self.hidden_neurons,
            dropout_rate=self.dropout_rate,
            batch_norm=self.batch_norm,
            preprocessing=self.preprocessing)

        self.model = self.model.to(self.device)
        # print model information
        print(self.model)
        self._train_autoencoder(train_loader)

        self.model.load_state_dict(self.best_model_dict)
        self.decision_scores_ = self.decision_function(X_train)
Fit detector. y is ignored in unsupervised methods.

        Parameters
        ----------
        X : numpy array of shape (n_samples, n_features)
            The input samples.

        y : Ignored
            Not used, present for API consistency by convention.

        Returns
        -------
        self : object
            Fitted estimator.
        
examples/temp_do_not_use.py