code: def show_batch(
      batch, 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`
      """
      (tensor_images, labels), records = unpack_batch(batch)
  
      for tensor_image, label, record in zip(
          tensor_images, labels, records
      ):
          image = tensor_to_image(tensor_image)
          record.set_img(image)
  
          if label is not None:
              mask = MaskArray(np.array(label["masks"].cpu().numpy()))
              record.detection.set_mask_array(mask)
  
      return show_samples(
          records, ncols=ncols, figsize=figsize, **show_samples_kwargs
      )
  
imports: from icevision.imports import *
  from icevision.utils import *
  from icevision.core import *
  from icevision.visualize import *
  from icevision.models.utils import *
label_desc: Use image node_id 1 for calling a global function with example usage: show_batch(batch, ncols, figsize) and returns: show_samples