xla::Shape on_host_shape =
      xla::ShapeUtil::DeviceShapeToHostShape(on_device_shape);
  xla::ScopedShapedBuffer shaped_buffer(on_host_shape, on_device_shape,
                                        client->backend().memory_allocator(),
                                        device_ordinal);
  for (auto& index_to_buffer : shaped_buffer.buffers()) {
    xla::Shape subshape =
        xla::ShapeUtil::GetSubshape(on_device_shape, index_to_buffer.first);

                                     /*source_base_index=*/{},
                                     /*target_base_index=*/{0});
    output = ScopedShapedBuffer(std::move(buffer), output.memory_allocator());
  }

  std::shared_ptr<se::Event> definition_event;
  if (use_multiple_streams_ && stream) {
    TF_ASSIGN_OR_RETURN(definition_event, stream->parent()->CreateEvent());

	} else {
		embeddedEncoder = scope.Serializer.EncoderForVersion(serializer.Serializer, contentKind.GroupVersion())
	}

	var memoryAllocator runtime.MemoryAllocator

	if encoderWithAllocator, supportsAllocator := embeddedEncoder.(runtime.EncoderWithAllocator); supportsAllocator {

// by caching created but unused items for later reuse, relieving pressure on the garbage collector.
//
// Usage:
//
//	memoryAllocator := runtime.AllocatorPool.Get().(*runtime.Allocator)
//	defer runtime.AllocatorPool.Put(memoryAllocator)
//
// A note for future:
//
//	consider introducing multiple pools for storing buffers of different sizes
//	perhaps this could allow us to be more efficient.

	return s.encode(obj, w, memAlloc)
}

// Encode serializes the provided object to the given writer.
func (s *Serializer) Encode(obj runtime.Object, w io.Writer) error {
	return s.encode(obj, w, &runtime.SimpleAllocator{})
}

func (s *Serializer) encode(obj runtime.Object, w io.Writer, memAlloc runtime.MemoryAllocator) error {

	}
	return obj, gvk, err
}

type encoderWithAllocator struct {
	encoder      EncoderWithAllocator
	memAllocator MemoryAllocator
}

// NewEncoderWithAllocator returns a new encoder
func NewEncoderWithAllocator(e EncoderWithAllocator, a MemoryAllocator) Encoder {
	return &encoderWithAllocator{
		encoder:      e,
		memAllocator: a,
	}
}

	if co, ok := obj.(runtime.CacheableObject); ok {
		return co.CacheEncode(c.Identifier(), func(obj runtime.Object, w io.Writer) error { return c.doEncode(obj, w, memAlloc) }, w)
	}
	return c.doEncode(obj, w, memAlloc)
}

func (c *codec) doEncode(obj runtime.Object, w io.Writer, memAlloc runtime.MemoryAllocator) error {
	encodeFn := c.encoder.Encode

	//     }
	//     return e.doEncode(obj, w)
	//   }
	Identifier() Identifier
}

// MemoryAllocator is responsible for allocating memory.
// By encapsulating memory allocation into its own interface, we can reuse the memory
// across many operations in places we know it can significantly improve the performance.
type MemoryAllocator interface {
	// Allocate reserves memory for n bytes.

#include "tensorflow/c/tf_status_helper.h"
#include "xla/stream_executor/executor_cache.h"
#include "xla/stream_executor/host_memory_allocation.h"
#include "xla/stream_executor/memory_allocation.h"
#include "xla/stream_executor/platform.h"
#include "xla/stream_executor/platform_manager.h"
#include "xla/stream_executor/stream.h"
#include "xla/stream_executor/stream_executor.h"