code: def make_cauchy_kernel(sigma: int) -> torch.Tensor:
      if sigma <= 0:
          raise ValueError(
              f"expecting positive sigma, got sigma={sigma}"
          )
      tail = int(sigma * 5)
      k = torch.tensor(
          [((x / sigma) ** 2 + 1) for x in range(-tail, tail + 1)]
      )
      k = torch.reciprocal(k)
      k = k / torch.sum(k)
      return k
  
file_path: MONAI/monai/losses/multi_scale.py
imports: from __future__ import annotations
  import torch
  from torch.nn.modules.loss import _Loss
  from monai.networks.layers import gaussian_1d, separable_filtering
  from monai.utils import LossReduction
label_desc: Use image node_id 2 for calling a global function with example usage: make_cauchy_kernel(sigma) and returns: k