//	         ::= <template-prefix> <template-args>
//	         ::= <template-param>
//	         ::= <decltype>
//	         ::=
//	         ::= <substitution>
//
//	<template-prefix> ::= <prefix> <(template) unqualified-name>
//	                  ::= <template-param>
//	                  ::= <substitution>
//
//	<decltype> ::= Dt <expression> E
//	           ::= DT <expression> E
func (st *state) prefix() AST {
	var a AST

  }
  // For LLVM style RTTI.
  static bool classof(const AbstractContext* ptr) {
    return ptr->getKind() == kGraph;
  }

 private:
  std::unique_ptr<TF_Graph, decltype(&TF_DeleteGraph)> graph_;
  std::vector<TF_Output> inputs_;
  string name_;
};

static TracingContext* GraphTracingFactory(const char* name, TF_Status* s) {
  return new GraphContext(name);
}

}

void CheckTFE_TensorHandleHasFloats(TFE_TensorHandle* handle,
                                    const std::vector<float>& expected_values) {
  std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(
      TF_NewStatus(), TF_DeleteStatus);
  TF_Tensor* t = TFE_TensorHandleResolve(handle, status.get());
  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());

// methods here, as part of making this class a singleton and the only way to
// register/use filesystems.

namespace tensorflow {

using UniquePtrTo_TF_Status =
    ::std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)>;

Status ModularFileSystem::NewRandomAccessFile(
    const std::string& fname, TransactionToken* token,
    std::unique_ptr<RandomAccessFile>* result) {
  if (ops_->new_random_access_file == nullptr)

// there are more than one), or "" otherwise.
string GPUDeviceName(TF_Session* session) {
  std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(
      TF_NewStatus(), TF_DeleteStatus);
  TF_Status* s = status.get();
  std::unique_ptr<TF_DeviceList, decltype(&TF_DeleteDeviceList)> list(
      TF_SessionListDevices(session, s), TF_DeleteDeviceList);
  TF_DeviceList* device_list = list.get();

               expected_max_outputs, *default_cancellation_manager_);
  auto result = Join(expected_output_shapes, status);
  if (TF_GetCode(status) != TF_OK) {
    std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> await_status(
        TF_NewStatus(), TF_DeleteStatus);
    // Wait until all pending nodes have completed since they may have a
    // reference to default_cancellation_manager_. We ignore the status return

          "char32_t",
          "class",
          "compl",
          "concept",
          "const",
          "const_cast",
          "constexpr",
          "continue",
          "decltype",
          "default",
          "delete",
          "do",
          "double",
          "dynamic_cast",
          "else",
          "enum",
          "explicit",
          "export",

using tensorflow::FunctionDef;
using tensorflow::Node;
using tensorflow::NodeBuilder;
using tensorflow::Status;
using tensorflow::errors::InvalidArgument;

namespace {
typedef std::unique_ptr<TF_Function, decltype(&TF_DeleteFunction)>
    UniqueFuncPtr;
}

// struct TF_Operation { tensorflow::Node node; };
static TF_Operation* ToTF_Operation(Node* node) {
  return static_cast<TF_Operation*>(static_cast<void*>(node));
}

  auto* tmp_var = new TmpVar;
  OP_REQUIRES(context, tmp_var,
              absl::ResourceExhaustedError("Could not allocate TmpVar."));
  tmp_var->name = unique_name;

  Status s;
  std::unique_ptr<TF_Tensor, decltype(&TF_DeleteTensor)> tmp_var_tf(
      tensorflow::TF_TensorFromTensor(tmp_var->val, &s), TF_DeleteTensor);
  OP_REQUIRES_OK(context, s);
  allocFunc(ctx, tmp_var_tf.get(), dtype, dims, num_dims, tf_status);

std::optional<StringRef> GetShardingStringFromVariant(
    const OpShardingVariant& sharding_or_op) {
  return std::visit(
      [](auto&& sharding_or_op) -> std::optional<StringRef> {
        using T = std::decay_t<decltype(sharding_or_op)>;
        if constexpr (std::is_same_v<T, StringRef>) {
          return sharding_or_op;
        } else {
          return GetXlaShardingFromOperator(sharding_or_op);
        }
      },