def AE_input_batch_generation_sup(self, train_x, inlier_indices, batch_size, rng):
        '''
        batchs of samples. This is for csv data.
        Alternates between positive and negative pairs.
        '''
        rng = np.random.RandomState(self.seed)

        dim = train_x.shape[1]
        ref = np.empty((batch_size, dim))
        training_labels = np.empty((batch_size, dim))
        n_inliers = len(inlier_indices)
        for i in range(batch_size):
            sid = rng.choice(n_inliers, 1)
            ref[i] = train_x[inlier_indices[sid]]
            training_labels[i] = train_x[inlier_indices[sid]]
        return np.array(ref), np.array(training_labels, dtype=float)

        batchs of samples. This is for csv data.
        Alternates between positive and negative pairs.
        
adbench/baseline/FEAWAD/run.py