code: def test_calc_psnr_numpy(data_range: float, arange_4d_image: Tensor):
      reference = torch.randn_like(arange_4d_image)
  
      val2 = peak_signal_noise_ratio(
          arange_4d_image, reference, data_range=data_range
      )
  
      val1 = torch.tensor(
          calc_psnr_numpy(
              arange_4d_image.numpy(),
              reference.numpy(),
              data_range=data_range,
          )
      ).float()
  
      assert torch.allclose(val1, val2)
  
imports: import pytest
  import torch
  from torch import Tensor
  from torchmetrics.functional.image import peak_signal_noise_ratio
  from neuralcompression.metrics import calc_psnr, calc_psnr_numpy
label_desc: Use image node_id 2 for calling a global function with example usage: test_calc_psnr_numpy(data_range, arange_4d_image) without return types