code: def validation_epoch_end(self, outputs, prefix="val") -> Dict:
      self.step_count += 1
      losses = {
          k: torch.stack([x[k] for x in outputs]).mean()
          for k in self.loss_names
      }
      loss = losses["loss"]
      gen_metrics = {
          k: np.array([x[k] for x in outputs]).mean()
          for k in self.metric_names + ["gen_time", "gen_len"]
      }
      metrics_tensor: torch.FloatTensor = torch.tensor(
          gen_metrics[self.val_metric]
      ).type_as(loss)
      gen_metrics.update({k: v.item() for k, v in losses.items()})
  
      # fix for https://github.com/PyTorchLightning/pytorch-lightning/issues/2424
      if dist.is_initialized():
          dist.all_reduce(metrics_tensor, op=dist.ReduceOp.SUM)
          metrics_tensor = metrics_tensor / dist.get_world_size()
          gen_metrics.update({self.val_metric: metrics_tensor.item()})
  
      losses.update(gen_metrics)
      metrics = {f"{prefix}_avg_{k}": x for k, x in losses.items()}
      metrics["step_count"] = self.step_count
      self.save_metrics(
          metrics, prefix
      )  # writes to self.metrics_save_path
      preds = flatten_list([x["preds"] for x in outputs])
      return {
          "log": metrics,
          "preds": preds,
          f"{prefix}_loss": loss,
          f"{prefix}_{self.val_metric}": metrics_tensor,
      }
  
imports: import argparse
  import logging
  import os
  import sys
  import time
  from collections import defaultdict
  from pathlib import Path
  from typing import Any, Dict, List, Tuple
  import numpy
  import pytorch_lightning
  import torch
  import torch.distributed
  import torch.distributed
  from pytorch_lightning.plugins.training_type import DDPPlugin
  from torch.utils.data import DataLoader
  from transformers import AutoConfig, AutoTokenizer, BartForConditionalGeneration, BatchEncoding, RagConfig, RagSequenceForGeneration, RagTokenForGeneration, RagTokenizer, T5ForConditionalGeneration
  from transformers import logging
  from transformers.integrations import is_ray_available
  from callbacks_rag import get_checkpoint_callback, get_early_stopping_callback, Seq2SeqLoggingCallback
  from distributed_pytorch_retriever import RagPyTorchDistributedRetriever
  from utils_rag import calculate_exact_match, flatten_list, get_git_info, is_rag_model, lmap, pickle_save, save_git_info, save_json, set_extra_model_params, Seq2SeqDataset
  from lightning_base import BaseTransformer, add_generic_args, generic_train
label_desc: Use image node_id 8 for calling the GenerativeQAModule obj's underlying member method code with example usage: obj.validation_epoch_end(outputs, prefix) and returns: dict