// TensorFlow Resource Handle Interfaces.
//===----------------------------------------------------------------------===//

def TF_ResourceHandleAllocatorInterface : OpInterface<"ResourceHandleAllocatorInterface"> {
  let description = [{
    A resource handle allocator operation is one that creates a resource handle,
    or looks up and reuses an existing resource handle.
  }];

  let methods = [

  // a resource instance ID. That means, they will be treated as non-aliasing.
  // This is correct for all current cases. A problematic case could be if we
  // had two ops A and B, A has the `ResourceHandleAllocatorInterface` and B has
  // not, and both ops might return a handle to the same resource (depending on
  // attributes). In this case, the return value of A would get a different ID

template <typename ConcreteType>
class UniqueResourceAllocation
    : public TraitBase<ConcreteType, UniqueResourceAllocation> {
 public:
  // Implements method required for `ResourceHandleAllocatorInterface`.
  llvm::SmallVector<mlir::TF::ResourceHandleValueAndId>
  GetResourceHandleValueAndIdList(
      llvm::SmallDenseMap<mlir::TF::ResourceHandle, int64_t>&
          resource_handle_id_map,

    }
  });

  llvm::SmallDenseMap<ResourceHandle, int64_t> resource_handle_id_map;
  func_op.walk([&](Operation* op) {
    if (auto resource_alloc = dyn_cast<ResourceHandleAllocatorInterface>(op)) {
      llvm::SmallVector<ResourceHandleValueAndId, 4> resources =
          resource_alloc.GetResourceHandleValueAndIdList(
              resource_handle_id_map, next_unique_instance_id);