// that handles the updated value for an resource argument.
LogicalResult LiftArgRetResourcesForFunction(
    func::FuncOp func_op,
    const llvm::SmallDenseMap<int64_t, Type>& resource_data_types,
    llvm::function_ref<void(int64_t, Value)> handle_updated_arg_value) {
  RegionResourceHoister hoister(func_op);
  if (failed(hoister.Analyze())) return failure();

  // Each of these resources could be read or written in the function. If its

  TFE_Context* ctx = TFE_NewContext(opts, status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TFE_DeleteContextOptions(opts);

  string function_def = MatMulFunction();
  TFE_ContextAddFunctionDef(ctx, function_def.data(), function_def.size(),
                            status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  for (bool clear_cache : {true, false, true}) {

  }
  return v;
}

// Specification for an expected edge.
// src is either:
// - input name (as it appears in FunctionDef)
// - name of output tensor (in nested "add:z:0" format)
// dst is either:
// - output name (as it appears in FunctionDef)
// - <name_of_node>:<index_of_this_input_into_node> (this looks the same as
//      output tensor naming, but it the index is actually an input index)

  // Find subgraphs marked with 'group_attribute', and build a new
  // subgraph, one for each value of 'group_attribute'.
  Status SplitIntoSubgraphs(FunctionLibraryDefinition* library);

  // Build a FunctionDef for each subgraph, and add it 'library'. The values of
  // the 'group_attribute' annotations become the function names.
  // If 'reuse_existing_functions' is set, use an existing function with the
  // same name, if any.

// inside the function can be compiled.
TEST(XlaCompilationTest, PartitionedCallUnsupported) {
  FunctionDef compilable = FunctionDefHelper::Define(
      "CompilableFn", {"n_a:float", "n_b:float"}, {"n_c:float"}, {},
      {{{"n_c"}, "Add", {"n_a", "n_b"}, {{"T", DT_FLOAT}}}});
  FunctionDef uncompilable =
      FunctionDefHelper::Define("UncompilableFn", {"n_a:float"}, {"n_c:float"},

    )

  def _is_quantized_function(self, func: function_pb2.FunctionDef) -> bool:
    """Determine whether a FunctionDef is quantized.

    Args:
      func: A FunctionDef object.

    Returns:
      True iff `func` is quantized.
    """
    return func.signature.name.startswith('quantized_')

  def _is_composite_function(self, func: function_pb2.FunctionDef) -> bool:

// The return value points to memory that is only usable until the next
// mutation to *func.
TF_CAPI_EXPORT extern const char* TF_FunctionName(TF_Function* func);

// Write out a serialized representation of `func` (as a FunctionDef protocol
// message) to `output_func_def` (allocated by TF_NewBuffer()).
// `output_func_def`'s underlying buffer will be freed when TF_DeleteBuffer()
// is called.
//