//
// The data will be deallocated by a subsequent call to TF_DeleteTensor via:
//      (*deallocator)(data, len, deallocator_arg)
// Clients must provide a custom deallocator function so they can pass in
// memory managed by something like numpy.
//
// May return NULL (and invoke the deallocator) if the provided data buffer
// (data, len) is inconsistent with a tensor of the given TF_DataType

	}

	return a, err
}

// NewInMemory creates an in-memory allocator.
func NewInMemory(pr net.PortRange) (*PortAllocator, error) {
	return New(pr, func(max int, rangeSpec string, offset int) (allocator.Interface, error) {
		return allocator.NewAllocationMapWithOffset(max, rangeSpec, offset), nil
	})
}

// NewFromSnapshot allocates a PortAllocator and initializes it from a snapshot.

    host_alloc_attr.set_on_host(true);
    host_allocator_ = device_->GetAllocator(host_alloc_attr);

    AllocatorAttributes device_alloc_attr;
    device_alloc_attr.set_on_host(false);
    device_allocator_ = device_->GetAllocator(device_alloc_attr);
  }

 protected:
  std::unique_ptr<Device> device_;
  Allocator* host_allocator_;
  Allocator* device_allocator_;
};

// 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,

	"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(

	"k8s.io/kubernetes/pkg/registry/core/service/allocator"
	allocatorstore "k8s.io/kubernetes/pkg/registry/core/service/allocator/storage"
	"k8s.io/kubernetes/pkg/registry/core/service/portallocator"
	"k8s.io/kubernetes/pkg/registry/registrytest"
)

const (
	basePortRange = 30000
	sizePortRange = 2768
)

// license that can be found in the LICENSE file.

// Simple not-in-heap bump-pointer traceRegion allocator.

package runtime

import (
	"internal/runtime/atomic"
	"runtime/internal/sys"
	"unsafe"
)

// traceRegionAlloc is a thread-safe region allocator.
// It holds a linked list of traceRegionAllocBlock.
type traceRegionAlloc struct {
	lock     mutex

# Above WB, we can have write barriers.
< WB
# Below WB is the write barrier implementation.
< wbufSpans;

# Span allocator
stackLarge,
  stackpool,
  wbufSpans
# Above mheap is anything that can call the span allocator.
< mheap;
# Below mheap is the span allocator implementation.
#
# Specials: we're allowed to allocate a special while holding

    BuildableType<"$_builder.getType<::tfrt::fallback::TFTensorType>()"> {
  let description = [{
    `!tfrt_fallback.tf_tensor type` represents a tensorflow::Tensor.
  }];
}

// This corresponds to tensorflow::Allocator.
def TFAllocatorType : DialectType<Fallback_Dialect,
    CPred<"$_self.isa<::tfrt::fallback::TFAllocatorType>()">, "!tfrt_fallback.tf_allocator type">,
    BuildableType<"$_builder.getType<::tfrt::fallback::TFAllocatorType>()"> {