static char empty;
  int64_t nelems = 1;
  std::vector<int64_t> dims;
  dims.reserve(shape.dims());
  for (int i = 0; i < shape.dims(); ++i) {
    dims.push_back(shape.dim_size(i));
    nelems *= shape.dim_size(i);
  }
  CHECK_EQ(nelems, 0);
  return TF_NewTensor(
      dtype, reinterpret_cast<const int64_t*>(dims.data()), shape.dims(),
      reinterpret_cast<void*>(&empty), 0, [](void*, size_t, void*) {}, nullptr);

- Update to latest cAdvisor 0.55.0 in our vendor dependencies ([#135829](https://github.com/kubernetes/kubernetes/pull/135829), [@dims](https://github.com/dims)) [SIG Node]
- Using pytorch based e2e integration test instead of tensorflow in some node e2e CI tests. ([#136397](https://github.com/kubernetes/kubernetes/pull/136397), [@dims](https://github.com/dims)) [SIG Testing]

* Increase request timeout based on termination grace period ([#31275](https://github.com/kubernetes/kubernetes/pull/31275), [@dims](https://github.com/dims))
* Fixed two issues of kubectl bash completion. ([#31135](https://github.com/kubernetes/kubernetes/pull/31135), [@xingzhou](https://github.com/xingzhou))

* promote contrib/mesos to incubator ([#33658](https://github.com/kubernetes/kubernetes/pull/33658), [@deads2k](https://github.com/deads2k))