code: def test_RealNVPLayer():
      """
      This test should evaluate if the transformation its being applied
      correctly. When computing the logarithm of the determinant jacobian matrix
      the result must be zero for any distribution when performing the first forward
      and inverse pass (initialized). This is the expected
      behavior since nothing is being learned yet.
  
      input shape: (samples, dim)
      output shape: (samples, dim)
  
      """
  
      dim = 2
      samples = 96
      data = MultivariateNormal(torch.zeros(dim), torch.eye(dim))
      tensor = data.sample(torch.Size((samples, dim)))
  
      layers = 4
      hidden_size = 16
      masks = F.one_hot(
          torch.tensor([i % 2 for i in range(layers)])
      ).float()
      layers = nn.ModuleList(
          [RealNVPLayer(mask, hidden_size) for mask in masks]
      )
  
      for layer in layers:
          _, inverse_log_det_jacobian = layer.inverse(tensor)
          inverse_log_det_jacobian = (
              inverse_log_det_jacobian.detach().numpy()
          )
          assert np.any(inverse_log_det_jacobian)
  
imports: import pytest
  import numpy
  import unittest
label_desc: Use image node_id 3 for calling a global function with example usage: test_RealNVPLayer() without return types