code: def _generative_step(self, batch: dict) -> dict:
      start_time = time.time()
      batch = BatchEncoding(batch).to(device=self.model.device)
      generated_ids = self.model.generate(
          batch["input_ids"],
          attention_mask=batch["attention_mask"],
          do_deduplication=False,  # rag specific parameter
          use_cache=True,
          min_length=1,
          max_length=self.target_lens["val"],
      )
  
      gen_time = (time.time() - start_time) / batch["input_ids"].shape[
          0
      ]
      preds: List[str] = self.ids_to_clean_text(generated_ids)
      target: List[str] = self.ids_to_clean_text(
          batch["decoder_input_ids"]
      )
      loss_tensors = self._step(batch)
      base_metrics = {
          name: loss
          for name, loss in zip(self.loss_names, loss_tensors)
      }
      gen_metrics: Dict = self.calc_generative_metrics(preds, target)
  
      summ_len = np.mean(lmap(len, generated_ids))
      base_metrics.update(
          gen_time=gen_time,
          gen_len=summ_len,
          preds=preds,
          target=target,
          **gen_metrics,
      )
      return base_metrics
  
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 11 for calling the GenerativeQAModule obj's underlying member method code with example usage: obj._generative_step(batch) and returns: base_metrics