def METHOD_NAME(batch_shape, event_shape):
    x = torch.randn(batch_shape + event_shape + event_shape)
    precision = x.matmul(x.transpose(-2, -1))
    actual = precision_to_scale_tril(precision)
    expected = torch.linalg.cholesky(precision.inverse())
    assert_close(actual, expected)