code: def test_target_sizes():
      # Should return <ndarray>s
      tens_400_400 = caer.resize(cv_tens, target_size=(400, 400))
      tens_304_339 = caer.resize(cv_tens, target_size=(304, 339))
      tens_199_206 = caer.resize(cv_tens, target_size=(199, 206))
  
      # Should return <caer.Tensor>s
      caer_tens_400_400 = caer.resize(caer_tens, target_size=(400, 400))
      caer_tens_304_339 = caer.resize(caer_tens, target_size=(304, 339))
      caer_tens_199_206 = caer.resize(caer_tens, target_size=(199, 206))
  
      assert tens_400_400.shape[:2] == (400, 400)
      assert tens_304_339.shape[:2] == (339, 304)
      assert tens_199_206.shape[:2] == (206, 199)
  
      # Type Asserts
      ## Using isinstance() often mistakes a caer.Tensor as an np.ndarray
      assert "caer.Tensor" in str(type(tens_400_400))
      assert "caer.Tensor" in str(type(tens_304_339))
      assert "caer.Tensor" in str(type(tens_199_206))
  
imports: import caer
  import cv2
  import os
label_desc: Use image node_id 1 for calling a global function with example usage: test_target_sizes() without return types