def METHOD_NAME():
    """
  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)))
    _, log_det_jacobian = Affine(dim).forward(tensor)
    _, inverse_log_det_jacobian = Affine(dim).inverse(tensor)
    # The first pass of the transformation should be 0
    log_det_jacobian = log_det_jacobian.detach().numpy()
    inverse_log_det_jacobian = inverse_log_det_jacobian.detach().numpy()
    zeros = np.zeros((samples,))
    assert np.array_equal(log_det_jacobian, zeros)
    assert np.array_equal(inverse_log_det_jacobian, zeros)