code: def single_gpu_test(model, data_loader):
      """Test model with a single gpu.
  
      This method tests model with a single gpu and displays test progress bar.
  
      Args:
          model (nn.Module): Model to be tested.
          data_loader (nn.Dataloader): Pytorch data loader.
  
      Returns:
          list: The prediction results.
      """
      model.eval()
      results = []
      dataset = data_loader.dataset
      prog_bar = mmcv.ProgressBar(len(dataset))
      for data in data_loader:
          with torch.no_grad():
              result = model(return_loss=False, **data)
          results.extend(result)
  
          # Assume result has the same length of batch_size
          # refer to https://github.com/open-mmlab/mmcv/issues/985
          batch_size = len(result)
          for _ in range(batch_size):
              prog_bar.update()
      return results
  
file_path: lavis/lavis/common/annotator/uniformer/mmcv/engine/test.py
imports: import os.path
  import pickle
  import shutil
  import tempfile
  import time
  import torch
  import torch.distributed
  import annotator.uniformer.mmcv
  from annotator.uniformer.mmcv.runner import get_dist_info
label_desc: Use image node_id 1 for calling a global function with example usage: single_gpu_test(model, data_loader) and returns: results