: AbstractContext(kind) {}
  ~ImmediateExecutionContext() override {}
};

namespace internal {
struct ImmediateExecutionContextDeleter {
  void operator()(ImmediateExecutionContext* p) const {
    if (p != nullptr) {
      p->Release();
    }
  }
};
}  // namespace internal

using ImmediateContextPtr =
    std::unique_ptr<ImmediateExecutionContext,

#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/platform/resource_loader.h"
#include "tensorflow/core/platform/test.h"
#include "tensorflow/core/protobuf/tensorflow_server.pb.h"

namespace tensorflow {
namespace {

TEST(CAPI_EXPERIMENTAL, GetServerDefTest) {
  const std::string expected_text_proto(R"(cluster {
  job {
    name: "worker"
    tasks {
      key: 0
      value: "tpuserver:0"
    }

// namespace at MinIO level with this DNS entry. The global namespace in
// enforced by the Kubernetes Operator
func (c *OperatorDNS) List() (srvRecords map[string][]SrvRecord, err error) {
	return nil, ErrNotImplemented
}

// Get - Retrieves DNS records for a bucket.
// This is a No Op for Operator because, there is no intent to enforce global

==============================================================================*/

#ifndef TENSORFLOW_C_EAGER_DLPACK_H_
#define TENSORFLOW_C_EAGER_DLPACK_H_

#include "tensorflow/c/eager/c_api.h"

namespace tensorflow {

// PyCapsule name for DLPack Tensor
const char* const kDlTensorCapsuleName = "dltensor";

// Returns the DLDevice* for the given eager tensor handle.
//

on the following DNS name from within your cluster: {{ template "minio.fullname" . }}.{{ .Release.Namespace }}.svc.cluster.local To access MinIO from localhost, run the below commands: 1. export POD_NAME=$(kubectl get pods --namespace {{ .Release.Namespace }} -l "release={{ .Release.Name }}" -o jsonpath="{.items[0].metadata.name}") 2. kubectl port-forward $POD_NAME 9000 --namespace {{ .Release.Namespace }} Read more about port forwarding here: http://kubernetes.io/docs/user-guide/kubectl/kubectl_port-forward/...

on the following DNS name from within your cluster: {{ template "minio.fullname" . }}.{{ .Release.Namespace }}.svc.cluster.local To access MinIO from localhost, run the below commands: 1. export POD_NAME=$(kubectl get pods --namespace {{ .Release.Namespace }} -l "release={{ .Release.Name }}" -o jsonpath="{.items[0].metadata.name}") 2. kubectl port-forward $POD_NAME 9000 --namespace {{ .Release.Namespace }} Read more about port forwarding here: http://kubernetes.io/docs/user-guide/kubectl/kubectl_port-forward/...

on the following DNS name from within your cluster: {{ template "minio.fullname" . }}.{{ .Release.Namespace }}.svc.cluster.local To access MinIO from localhost, run the below commands: 1. export POD_NAME=$(kubectl get pods --namespace {{ .Release.Namespace }} -l "release={{ .Release.Name }}" -o jsonpath="{.items[0].metadata.name}") 2. kubectl port-forward $POD_NAME 9000 --namespace {{ .Release.Namespace }} Read more about port forwarding here: http://kubernetes.io/docs/user-guide/kubectl/kubectl_port-forward/...

#include "tensorflow/core/lib/llvm_rtti/llvm_rtti.h"
#include "tensorflow/core/platform/errors.h"
#include "tensorflow/core/platform/types.h"

using tensorflow::string;

namespace tensorflow {
namespace tracing {
typedef absl::flat_hash_map<std::string, tracing::FactoryFunction> FactoriesMap;

static FactoriesMap& GetFactories() {
  static FactoriesMap* factories = new FactoriesMap;
  return *factories;
}

}

/**
 * Subset of the elemental2 IThenable interface without the single-parameter overload, which allows
 * us to implement it using a default implementation in J2cl ListenableFuture.
 */
@JsType(isNative = true, namespace = JsPackage.GLOBAL, name = "IThenable")
interface IThenable<T extends @Nullable Object> {
  <V extends @Nullable Object> IThenable<V> then(
      @JsOptional @Nullable IThenOnFulfilledCallbackFn<? super T, ? extends V> onFulfilled,

#include "tensorflow/c/tf_status_internal.h"
#include "tensorflow/core/common_runtime/eager/tensor_handle.h"
#include "tensorflow/core/platform/status.h"

using tensorflow::string;

namespace {

std::vector<int64_t> TensorShapeAsVector(const tensorflow::TensorHandle& handle,
                                         absl::Status* status) {
  std::vector<int64_t> shape;
  int rank = -1;