class_docstr: CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
  (https://arxiv.org/abs/1905.04899)
class_docstr_tok: ["CutMix",":","Regularization","Strategy","to","Train","Strong","Classifiers","with","Localizable","Features","(","https",":","\/\/arxiv.org\/abs\/1905.04899",")"]
docstr: Get a random square box given a lambda value.
docstr_tok: ["Get","a","random","square","box","given","a","lambda","value","."]
code: def _get_rand_box(
      self, input_shape: Tuple[int], cutmix_lamda: float
  ) -> Tuple[int]:
      """
      Get a random square box given a lambda value.
      """
  
      ratio = (1 - cutmix_lamda) ** 0.5
      input_h, input_w = input_shape[-2:]
      cut_h, cut_w = int(input_h * ratio), int(input_w * ratio)
      cy = torch.randint(input_h, (1,)).item()
      cx = torch.randint(input_w, (1,)).item()
      yl = self._clip(cy - cut_h // 2, 0, input_h)
      yh = self._clip(cy + cut_h // 2, 0, input_h)
      xl = self._clip(cx - cut_w // 2, 0, input_w)
      xh = self._clip(cx + cut_w // 2, 0, input_w)
      return yl, yh, xl, xh
  
file_path: pytorchvideo/pytorchvideo/transforms/mix.py
imports: from typing import Any, Dict, Tuple
  import torch
  from pytorchvideo.transforms.functional import convert_to_one_hot
label_desc: Use image node_id 3 for calling the CutMix obj's underlying member method code with example usage: obj._get_rand_box(input_shape, cutmix_lamda) and returns: yl,  yh,  xl,  xh