code: def fivecrop_image2label(
      model: nn.Module, image: Tensor, crop_size: Tuple
  ) -> Tensor:
      """Test-time augmentation for image classification that takes five crops out of input tensor (4 on corners and central)
      and averages predictions from them.
  
      :param model: Classification model
      :param image: Input image tensor
      :param crop_size: Crop size. Must be smaller than image size
      :return: Averaged logits
      """
      input_aug = fivecrop_image_augment(image, crop_size)
      preds_aug = model(input_aug)
      output = fivecrop_label_deaugment(preds_aug)
      return output
  
imports: from collections import defaultdict
  from functools import partial
  from typing import Tuple, List, Optional, Union, Callable, Dict, Mapping
  import torch
  from torch import Tensor, nn
  from .None import functional
  from ..utils.support import pytorch_toolbelt_deprecated
label_desc: Use image node_id 5 for calling a global function with example usage: fivecrop_image2label(model, image, crop_size) and returns: output