2a8c7d1976fc74ff9f1075af31f4b9d0482bee53,tests/ops/test_welford.py,,test_welford_dense,#Any#Any#,39

Before Change

    samples = []
    for _ in range(n_samples):
        sample = dist.sample()
        samples.append(sample)
        w.update(sample)

    with optional(pytest.raises(RuntimeError), n_samples == 1):
        estimates = w.get_covariance(regularize=False).data.cpu().numpy()

After Change

    cov = torch.randn(dim_size, dim_size)
    cov = torch.mm(cov, cov.t())
    dist = torch.distributions.MultivariateNormal(loc=loc, covariance_matrix=cov)
    samples = dist.sample(torch.Size([n_samples]))
    for sample in samples:
        w.update(sample)

    with optional(pytest.raises(RuntimeError), n_samples == 1):

Instances