code: def generate_predictions(model, dataloader, accelerator):
      logits_and_targets = []
      for batch in dataloader:
          input, target = batch.values()
          with torch.no_grad():
              logit = model(input)
              logit, target = accelerator.gather_for_metrics(
                  (logit, target)
              )
              logits_and_targets.append((logit, target))
      logits, targs = [], []
      for logit, targ in logits_and_targets:
          logits.append(logit)
          targs.append(targ)
      logits, targs = torch.cat(logits), torch.cat(targs)
      return logits, targs
  
imports: import logging
  import math
  import os
  from copy import deepcopy
  import datasets
  import evaluate
  import torch
  import transformers
  from datasets import load_dataset
  from torch.utils.data import DataLoader, IterableDataset
  from transformers import AutoModelForSequenceClassification, AutoTokenizer
  from accelerate import Accelerator
  from accelerate.data_loader import DataLoaderDispatcher
  from accelerate.test_utils import RegressionDataset, RegressionModel, torch_device
  from accelerate.utils import set_seed
label_desc: Use image node_id 4 for calling a global function with example usage: generate_predictions(model, dataloader, accelerator) and returns: logits,  targs