code: def mixup(batch: Dict, lam: float = 0.5) -> Dict:
      """It apply mixup augmentation, making a weighted average between a tensor and some random element of the tensor
      taking random rows.
  
      :param batch: Tensor on which apply the mixup augmentation
      :param lam: weight in the linear combination between the original values and the random permutation
      """
      result = {}
      for key, value in batch.items():
          random_index = _get_random_index(value)
          result[key] = lam * value + (1 - lam) * value[random_index, :]
          result[key] = result[key].to(dtype=value.dtype)
      return result
  
imports: from typing import Dict
  import numpy
  import torch
label_desc: Use image node_id 1 for calling a global function with example usage: mixup(batch, lam) and returns: result