TF_Status* status) {
  auto* cc_ctx = reinterpret_cast<::tensorflow::OpKernelConstruction*>(ctx);
  tensorflow::NameAttrList function;
  auto cc_status = cc_ctx->GetAttr(attr_name, &function);
  if (!cc_status.ok()) {
    tsl::Set_TF_Status_from_Status(status, cc_status);
    return nullptr;
  }
  TF_Buffer* buffer = TF_NewBuffer();
  cc_status = tensorflow::MessageToBuffer(function, buffer);

    stats.num_allocs = c_stats.num_allocs;
    stats.bytes_in_use = c_stats.bytes_in_use;
    stats.peak_bytes_in_use = c_stats.peak_bytes_in_use;
    stats.largest_alloc_size = c_stats.largest_alloc_size;
    if (c_stats.has_bytes_limit) {
      stats.bytes_limit = c_stats.bytes_limit;
    }
    stats.bytes_reserved = c_stats.bytes_reserved;
    stats.peak_bytes_reserved = c_stats.peak_bytes_reserved;

  int VisibleDeviceCount() const override {
    int visible_device_count = 0;
    tensorflow::TF_StatusPtr c_status(TF_NewStatus());
    platform_fns_.get_device_count(&platform_, &visible_device_count,
                                   c_status.get());
    if (TF_GetCode(c_status.get()) != TF_OK) {
      LOG(ERROR) << TF_Message(c_status.get());
      return 0;
    }
    return visible_device_count;
  }

                         TF_Status* const status) -> void {
    *stream = new SP_Stream_st(1);
  };
  se_.destroy_stream = [](const SP_Device* const device,
                          SP_Stream stream) -> void { delete stream; };
  se_.create_event = [](const SP_Device* const device, SP_Event* event,
                        TF_Status* const status) -> void {
    *event = new SP_Event_st(2);
  };

		// started the container. Set the StartedAt time.
		cStatus.StartedAt = time.Unix(0, status.StartedAt)
	}
	if status.State == runtimeapi.ContainerState_CONTAINER_EXITED {
		cStatus.Reason = status.Reason
		cStatus.Message = status.Message
		cStatus.ExitCode = int(status.ExitCode)
		cStatus.FinishedAt = time.Unix(0, status.FinishedAt)
	}
	return cStatus
}

			limits = make(v1.ResourceList)
			if cStatus.Resources != nil && cStatus.Resources.CPULimit != nil {
				limits[v1.ResourceCPU] = cStatus.Resources.CPULimit.DeepCopy()
			} else {
				determineResource(v1.ResourceCPU, container.Resources.Limits, oldStatus.Resources.Limits, limits)
			}
			if cStatus.Resources != nil && cStatus.Resources.MemoryLimit != nil {
				limits[v1.ResourceMemory] = cStatus.Resources.MemoryLimit.DeepCopy()

#include "absl/status/status.h"
#include "absl/strings/string_view.h"
#include "flatbuffers/flatbuffer_builder.h"  // from @flatbuffers
#include "tensorflow/c/kernels.h"
#include "tensorflow/c/tf_status.h"
#include "tensorflow/compiler/mlir/lite/debug/debug_options.pb.h"
#include "tensorflow/compiler/mlir/lite/metrics/error_collector.h"
#include "tensorflow/compiler/mlir/lite/python/flatbuffer_to_mlir.h"

  ApiConverter::StackHelper<XLA_Shape> tpu_shape;
  StatusHelper status;
  stream_executor::tpu::ExecutorApiFn()->XlaShapeToTpuShapeRepresentationFn(
      &se_shape.value, type, use_fast_memory, &tpu_shape.value,
      status.c_status);
  if (!status.status().ok()) {
    return status.status();
  }
  return tpu_shape.AsCpp<xla::Shape>();
}

// Given a tensor, returns the shape of its representation on device,

			cStatus := &runtimeapi.ContainerStatus{
				Id:        cid.ID,
				Metadata:  meta,
				Image:     imageSpec,
				State:     runtimeapi.ContainerState_CONTAINER_RUNNING,
				CreatedAt: createdAt,
				StartedAt: startedAt,
				User:      test.input,
			}
			actual := toKubeContainerStatus(cStatus, cid.Type)
			assert.EqualValues(t, test.expected, actual.User, desc)

}

func containerSucceeded(c *v1.Container, podStatus *kubecontainer.PodStatus) bool {
	cStatus := podStatus.FindContainerStatusByName(c.Name)
	if cStatus == nil || cStatus.State == kubecontainer.ContainerStateRunning {
		return false
	}
	return cStatus.ExitCode == 0
}

func isInPlacePodVerticalScalingAllowed(pod *v1.Pod) bool {