code: def show_batch(
      batch_and_records,
      ncols: int = 1,
      figsize=None,
      **show_samples_kwargs,
  ):
      """Show a single batch from a dataloader.
      # Arguments
          show_samples_kwargs: Check the parameters from `show_samples`
      """
      batch, records = batch_and_records
  
      # In inference, both "img" and "img_metas" are lists. Check out the `build_infer_batch()` definition
      # We need to convert that to a batch similar to train and valid batches
      if isinstance(batch["img"], list):
          batch = {
              "img": batch["img"][0],
              "img_metas": batch["img_metas"][0],
          }
  
      for tensor_image, record in zip(batch["img"][:], records):
          image = mmdet_tensor_to_image(tensor_image)
          record.set_img(image)
  
      return show_samples(
          records, ncols=ncols, figsize=figsize, **show_samples_kwargs
      )
  
label_desc: Use image node_id 1 for calling a global function with example usage: show_batch(batch_and_records, ncols, figsize) and returns: show_samples