def evaluate(self, data_loader):
        """
        Evaluate the deep learning model.

        Parameters
        ----------
        data_loader : torch.utils.data.DataLoader
            The data loader for evaluating the model.

        Returns
        -------
        outlier_scores : numpy array of shape (n_samples,)
            The outlier scores of the input samples.
        """
        self.evaluating_prepare()
        anamoly_scores = []
        with torch.no_grad():
            for batch_data in data_loader:
                score = self.evaluating_forward(batch_data)
                anamoly_scores.append(score)
        anamoly_scores = np.concatenate(anamoly_scores)
        return anamoly_scores

        Evaluate the deep learning model.

        Parameters
        ----------
        data_loader : torch.utils.data.DataLoader
            The data loader for evaluating the model.

        Returns
        -------
        outlier_scores : numpy array of shape (n_samples,)
            The outlier scores of the input samples.
        
pyod/models/base_dl.py