int dim1 = 1 << 16;
    int dim2 = 1 << 15;
    assertThat(dim1 * dim2).isLessThan(0);
    testConcatOverflow(dim1, dim2);
  }

  @GwtIncompatible // different overflow behavior; could probably be made to work by using ~~
  public void testConcat_overflow_nonNegative() {
    int dim1 = 1 << 16;
    int dim2 = 1 << 16;
    assertThat(dim1 * dim2).isAtLeast(0);

    int dim1 = 1 << 16;
    int dim2 = 1 << 15;
    assertThat(dim1 * dim2).isLessThan(0);
    testConcatOverflow(dim1, dim2);
  }

  @GwtIncompatible // different overflow behavior; could probably be made to work by using ~~
  public void testConcat_overflow_nonNegative() {
    int dim1 = 1 << 16;
    int dim2 = 1 << 16;
    assertThat(dim1 * dim2).isAtLeast(0);

  TestHandleFromDLPack(status, ctx, {4}, {});
  TestHandleFromDLPack(status, ctx, {4}, {1});
  TestHandleFromDLPack(status, ctx, {4, 3, 2}, {});
  TestHandleFromDLPack(status, ctx, {4, 3, 2}, {6, 2, 1});
  // Test that dims with size=1 can have any stride.
  TestHandleFromDLPack(status, ctx, {1}, {1});
  TestHandleFromDLPack(status, ctx, {1}, {0});
  TestHandleFromDLPack(status, ctx, {4, 1, 2}, {2, 1, 1});

* Fix issue where on re-registration of device plugin, `allocatable` was not getting updated. This issue makes devices invisible to the Kubelet if device plugin restarts. Only work-around, if this fix is not there, is to restart...

- 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]

    size_t size = TFE_TensorHandleDeviceMemorySize(copy, status);
    CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
    int64_t dims[] = {2, 2};
    TFE_TensorHandle* copy_aliased = TFE_NewTensorHandleFromDeviceMemory(
        ctx, name, TF_FLOAT, dims, 2, data, size, &Deleter, copy, status);
    CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
    TFE_TensorHandle* on_host =

  // `memory_releaser` will be called on destruction, and it's responsible for
  // cleaning up the underlying buffer.
  virtual AbstractTensorInterface* CreateTensor(
      DataType dtype, const int64_t* dims, int num_dims, void* data, size_t len,
      MemoryReleaser memory_releaser, void* memory_releaser_arg) = 0;

  // Create a handle to wrap and manage a Tensor
  virtual ImmediateExecutionTensorHandle* CreateLocalHandle(

}

TF_Tensor* TFE_AllocateHostTensor(TFE_Context* ctx, TF_DataType dtype,
                                  const int64_t* dims, int num_dims,
                                  TF_Status* status) {
  std::vector<int64_t> dimvec(num_dims);
  for (int i = 0; i < num_dims; ++i) {
    dimvec[i] = static_cast<int64_t>(dims[i]);
  }

  if (ctx == nullptr) {
    status->status = tensorflow::errors::InvalidArgument("Invalid Context");

* kubeadm: Fix pause image to not use architecture, as it is a manifest list ([#65920](https://github.com/kubernetes/kubernetes/pull/65920), [@dims](https://github.com/dims))
* bazel deb package bugfix: The kubeadm deb package now reloads the kubelet after installation ([#65554](https://github.com/kubernetes/kubernetes/pull/65554), [@rdodev](https://github.com/rdodev))

  TFE_DeleteContextOptions(opts);

  std::vector<int64_t> dims(4, 1);
  TFE_Op* op = TFE_NewOp(ctx, "AvgPool", status);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  TF_Tensor* tensor =
      TF_AllocateTensor(TF_FLOAT, dims.data(), dims.size(), sizeof(float));
  float tensor_data[] = {1};