WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/

package ipallocator

import (
	"errors"
	"fmt"
	"net"

	api "k8s.io/kubernetes/pkg/apis/core"
)

// Interface manages the allocation of IP addresses out of a range. Interface
// should be threadsafe.

// Returns allocator from platform info if non-null, or populate and return a
// pointer to the allocator adapter with allocator from context.
//
// This is necessary because for XLA devices the underlying TF allocator returns
// dummy tensors.
//
// `stream` parameter is nullable when running on host.
std::shared_ptr<se::DeviceMemoryAllocator> GetAllocator(
    DeviceBase* device, se::Stream* stream,

#include "tensorflow/core/protobuf/error_codes.pb.h"

namespace tensorflow {
namespace {

class DummyDevice : public DeviceBase {
 public:
  explicit DummyDevice(Env* env) : DeviceBase(env) {}
  Allocator* GetAllocator(AllocatorAttributes /*attr*/) override {
    return cpu_allocator();
  }
};

// Helper for comparing output and expected output
void ExpectSummaryMatches(const Summary& actual, const string& expected_str) {

      &fbody->record->fdef().signature(), node_def, fbody->arg_types,
      fbody->ret_types);
  OpKernelConstruction construction(DeviceType(dev->device_type()), dev,
                                    dev->GetAllocator(AllocatorAttributes()),
                                    flr, dev->resource_manager(), props,
                                    input_memory_types, output_memory_types,

}
def TFL_LSTMKernelTypeAttr : EnumAttr<TFL_Dialect, TFL_LSTMKernelType,
    "lstm_kernel_type_attr">;

def I32ArrayParameter :
    AttrOrTypeParameter<"::llvm::ArrayRef<int32_t>", ""> {
  let allocator = [{$_dst = $_allocator.copyInto($_self);}];
  let cppStorageType = "::llvm::SmallVector<int32_t>";
  let parser = "::mlir::TFL::parseI32Array($_parser)";
  let printer = "$_printer << '[' << $_self << ']'";
}

#include "tensorflow/core/platform/test.h"

namespace tensorflow {
namespace {

class DummyDevice : public DeviceBase {
 public:
  explicit DummyDevice(Env* env) : DeviceBase(env) {}
  Allocator* GetAllocator(AllocatorAttributes /*attr*/) override {
    return cpu_allocator();
  }
};

void TestBitcastOp(Tensor* input_tensor, DataType out_type,
                   TensorShape expected_shape, error::Code expected_code) {

	"k8s.io/klog/v2"
)

// CIDRAllocatorType is the type of the allocator to use.
type CIDRAllocatorType string

const (
	// RangeAllocatorType is the allocator that uses an internal CIDR
	// range allocator to do node CIDR range allocations.
	RangeAllocatorType CIDRAllocatorType = "RangeAllocator"
	// CloudAllocatorType is the allocator that uses cloud platform
	// support to do node CIDR range allocations.

  return new PJRT_Buffer{std::move(*buffer), c_api_client->pjrt_c_client()};
}

TEST(TensorPjRtBufferUtilTest, GetPjRtCBufferFromTensorNoBuffer) {
  auto allocator = std::make_unique<AsyncValueAllocator>();
  tensorflow::Tensor tensor(allocator.get(), DT_FLOAT, {1});

  EXPECT_THAT(
      GetPjRtCBufferFromTensor(&tensor),
      StatusIs(error::INTERNAL, HasSubstr(absl::StrCat(

func.func @custom_allocator(%ch: !tfrt.chain, %arg: !tfrt_fallback.tf_tensor, %allocator: !tfrt_fallback.tf_allocator) -> (!tfrt.chain, !tfrt_fallback.tf_tensor) {
  // CHECK: tfrt_fallback_async.executeop.allocator(%{{.*}}) key(200) cost(100) device("cpu") "tf.Cast"(%{{.*}}) {Truncate = false, T = f32} : 1
  %0 = tfrt_fallback_async.executeop.allocator(%allocator) key(200) cost(100) device("cpu") "tf.Cast"(%arg) {Truncate = false, T = f32} : 1

	destroyFn func()
}

// Etcd implements allocator.Interface and rangeallocation.RangeRegistry
var _ allocator.Interface = &Etcd{}
var _ rangeallocation.RangeRegistry = &Etcd{}

// NewEtcd returns an allocator that is backed by Etcd and can manage
// persisting the snapshot state of allocation after each allocation is made.