message: removed test_wasserstein_distance_grad to be consistent with master
diff: @@ -159,26 +159,3 @@ def test_wasserstein_distance_hera():
  _basic_wasserstein(hera_wrap(delta=1e-12), 1e-12, test_matching=False)
  _basic_wasserstein(hera_wrap(delta=.1), .1, test_matching=False)
  -def test_wasserstein_distance_grad():
  - import torch
  -
  - diag1 = torch.tensor([[2.7, 3.7], [9.6, 14.0], [34.2, 34.974]], requires_grad=True)
  - diag2 = torch.tensor([[2.8, 4.45], [9.5, 14.1]], requires_grad=True)
  - diag3 = torch.tensor([[2.8, 4.45], [9.5, 14.1]], requires_grad=True)
  - assert diag1.grad is None and diag2.grad is None and diag3.grad is None
  - dist12 = pot(diag1, diag2, internal_p=2, order=2, enable_autodiff=True, keep_essential_parts=False)
  - dist30 = pot(diag3, torch.tensor([]), internal_p=2, order=2, enable_autodiff=True, keep_essential_parts=False)
  - dist12.backward()
  - dist30.backward()
  - assert not torch.isnan(diag1.grad).any() and not torch.isnan(diag2.grad).any() and not torch.isnan(diag3.grad).any()
  - diag4 = torch.tensor([[0., 10.]], requires_grad=True)
  - diag5 = torch.tensor([[1., 11.], [3., 4.]], requires_grad=True)
  - dist45 = pot(diag4, diag5, internal_p=1, order=1, enable_autodiff=True, keep_essential_parts=False)
  - assert dist45 == 3.
  - dist45.backward()
  - assert np.array_equal(diag4.grad, [[-1., -1.]])
  - assert np.array_equal(diag5.grad, [[1., 1.], [-1., 1.]])
  - diag6 = torch.tensor([[5., 10.]], requires_grad=True)
  - pot(diag6, diag6, internal_p=2, order=2, enable_autodiff=True, keep_essential_parts=False).backward()
  - # https://github.com/jonasrauber/eagerpy/issues/6
  - # assert np.array_equal(diag6.grad, [[0., 0.]])
  