code: def evaluate_model(
      model: nn.Module,
      criterion,
      test_dataloader: Union[DataLoader, List[DataLoader]],
      num_labels: int,
      task_name: str,
      eval_splits: List[str],
      booster: Booster,
      coordinator: DistCoordinator,
  ):
      metric = evaluate.load(
          "glue",
          task_name,
          process_id=coordinator.rank,
          num_process=coordinator.world_size,
      )
      model.eval()
  
      def evaluate_subset(dataloader: DataLoader):
          use_pipeline = (
              isinstance(booster.plugin, HybridParallelPlugin)
              and booster.plugin.pp_size > 1
          )
          is_pp_last_device = (
              use_pipeline
              and booster.plugin.stage_manager.is_last_stage(
                  ignore_chunk=True
              )
          )
  
          accum_loss = torch.zeros(1, device=get_current_device())
          for batch in dataloader:
              batch = move_to_cuda(batch)
              labels = batch["labels"]
              if use_pipeline:
                  pg_mesh = booster.plugin.pg_mesh
                  pp_group = booster.plugin.pp_group
                  current_pp_group_ranks = pg_mesh.get_ranks_in_group(
                      pp_group
                  )
                  current_rank = dist.get_rank()
                  batch = iter([batch])
  
                  outputs = booster.execute_pipeline(
                      batch,
                      model,
                      criterion,
                      return_loss=True,
                      return_outputs=True,
                  )
  
                  if is_pp_last_device:
                      logits = outputs["outputs"]["logits"]
                      val_loss = outputs["loss"]
                      accum_loss.add_(val_loss)
  
                      if num_labels > 1:
                          preds = torch.argmax(logits, axis=1)
                      elif num_labels == 1:
                          preds = logits.squeeze()
  
                      dist.broadcast_object_list(
                          [preds, val_loss],
                          src=current_pp_group_ranks[-1],
                          group=pp_group,
                      )
  
                      metric.add_batch(
                          predictions=preds, references=labels
                      )
                  elif current_rank in current_pp_group_ranks:
                      object_list = [None, None]
                      dist.broadcast_object_list(
                          object_list,
                          src=current_pp_group_ranks[-1],
                          group=pp_group,
                      )
  
                      metric.add_batch(
                          predictions=object_list[0].to(
                              get_current_device()
                          ),
                          references=labels,
                      )
                      accum_loss.add_(
                          object_list[1].to(get_current_device())
                      )
  
              else:
                  batch = move_to_cuda(batch)
                  outputs = model(**batch)
                  val_loss, logits = outputs[:2]
                  accum_loss.add_(val_loss)
  
                  if num_labels > 1:
                      preds = torch.argmax(logits, axis=1)
                  elif num_labels == 1:
                      preds = logits.squeeze()
  
                  metric.add_batch(predictions=preds, references=labels)
  
          results = metric.compute()
          dist.all_reduce(accum_loss.div_(len(dataloader)))
          if coordinator.is_master() and results is not None:
              results["loss"] = (
                  accum_loss.item() / coordinator.world_size
              )
  
          return results
  
      if isinstance(test_dataloader, DataLoader):
          return evaluate_subset(test_dataloader)
      else:
          assert len(test_dataloader) == len(eval_splits)
          final_results = {}
          for split, sub_loader in zip(eval_splits, test_dataloader):
              results = evaluate_subset(sub_loader)
              final_results.update(
                  {f"{k}_{split}": v for k, v in results.items()}
              )
          return final_results
  
imports: import argparse
  from typing import Callable, List, Union
  import evaluate
  import torch
  import torch.distributed
  import torch.nn
  from data import GLUEDataBuilder
  from torch.optim import Optimizer
  from torch.optim.lr_scheduler import _LRScheduler
  from torch.utils.data import DataLoader
  from tqdm import tqdm
  from transformers import AlbertForSequenceClassification, AutoConfig, BertForSequenceClassification, get_linear_schedule_with_warmup
  import colossalai
  from colossalai.booster import Booster
  from colossalai.booster.plugin import GeminiPlugin, HybridParallelPlugin, LowLevelZeroPlugin, TorchDDPPlugin
  from colossalai.cluster import DistCoordinator
  from colossalai.nn.optimizer import HybridAdam
  from colossalai.utils import get_current_device
label_desc: Use image node_id 2 for calling a global function with example usage: evaluate_model(model, criterion, test_dataloader, num_labels, task_name, eval_splits, booster, coordinator) and returns: results, evaluate_subset, final_results