code: def test_data_container_keypoints_rescale_to_torch():
      kpts_data = np.array(
          [[100, 20], [1023, 80], [20, 20], [100, 700]]
      ).reshape((4, 2))
      kpts = slc.Keypoints(kpts_data, 768, 1024)
      ppl = slc.Stream()
      k, label = ppl({"keypoints": kpts, "label": 1}, as_dict=False)
      assert isinstance(k, torch.FloatTensor)
      np.testing.assert_almost_equal(k.max().item() * 1023, 1023)
      np.testing.assert_almost_equal(k.min().item() * 1023, 20)
      assert label == 1
  
imports: import itertools
  import random
  import cv2
  import numpy
  import pytest
  import torch
  import solt.core
  import solt.transforms
  import solt.utils
  from .fixtures import *
label_desc: Use image node_id 47 for calling a global function with example usage: test_data_container_keypoints_rescale_to_torch() without return types