code: def _eval_end(self, outputs) -> tuple:
      val_loss_mean = (
          torch.stack([x["val_loss"] for x in outputs])
          .mean()
          .detach()
          .cpu()
          .item()
      )
      preds = np.concatenate([x["pred"] for x in outputs], axis=0)
  
      if self.hparams.glue_output_mode == "classification":
          preds = np.argmax(preds, axis=1)
      elif self.hparams.glue_output_mode == "regression":
          preds = np.squeeze(preds)
  
      out_label_ids = np.concatenate(
          [x["target"] for x in outputs], axis=0
      )
      out_label_list = [[] for _ in range(out_label_ids.shape[0])]
      preds_list = [[] for _ in range(out_label_ids.shape[0])]
  
      results = {
          **{"val_loss": val_loss_mean},
          **compute_metrics(self.hparams.task, preds, out_label_ids),
      }
  
      ret = {k: v for k, v in results.items()}
      ret["log"] = results
      return ret, preds_list, out_label_list
  
imports: import argparse
  import glob
  import logging
  import os
  import time
  from argparse import Namespace
  import numpy
  import torch
  from torch.utils.data import DataLoader, TensorDataset
  from lightning_base import BaseTransformer, add_generic_args, generic_train
  from transformers import glue_compute_metrics
  from transformers import glue_convert_examples_to_features
  from transformers import glue_output_modes
  from transformers import glue_processors
  from transformers import glue_tasks_num_labels
label_desc: Use image node_id 7 for calling the GLUETransformer obj's underlying member method code with example usage: obj._eval_end(outputs) and returns: ret,  preds_list,  out_label_list