}
      SymbolTable::setSymbolName(func_op, new_func_name);
    }
    // Move all functions from XlaCallModuleOp's stablehlo module, to parent
    // module. Also marks the stablehlo module entry function as private.
    func::FuncOp main_fn;
    for (auto func_op : stablehlo_module_op.get().getOps<func::FuncOp>()) {
      func::FuncOp cloned_func_op = func_op.clone();
      if (IsShloMainFuncOp(cloned_func_op)) {

			startLine: start.Line,
			startCol:  start.Column,
			endLine:   end.Line,
			endCol:    end.Column,
		}
		v.funcs = append(v.funcs, fe)
	}
	return v
}

// coverage returns the fraction of the statements in the function that were covered, as a numerator and denominator.
func (f *FuncExtent) coverage(profile *cover.Profile) (num, den int64) {

//
//	 1 func main() {
//	 2     f()
//	 3 }
//	 4 func f() {
//	 5     g()
//	 6 }
//	 7 func g() {
//	 8     h()
//	 9     h()
//	10 }
//	11 func h() {
//	12     println("H")
//	13 }
//
// Assuming the global tree starts empty, inlining will produce the
// following tree:
//
//	[]InlinedCall{
//	  {Parent: -1, Func: "f", Pos: <line 2>},

// function.
bool ShouldIncludeInMainFunction(func::FuncOp func_op) {
  return !func_op.isPrivate() && IsExported(func_op) &&
         IsEntryFunction(func_op);
}

// Sets a function to be private so it can be referred internally.
void SetFunctionPrivate(func::FuncOp func) {
  func.setVisibility(SymbolTable::Visibility::Private);

Por exemplo, se você precisar armazená-lo em um banco de dados.

Para isso, **FastAPI** fornece uma função `jsonable_encoder()`.

## Usando a função `jsonable_encoder`

Vamos imaginar que você tenha um banco de dados `fake_db` que recebe apenas dados compatíveis com JSON.

      getAnalysis<TF::ResourceAliasAnalysis>();

  llvm::SmallDenseMap<func::FuncOp, PerFunctionResult, 4> per_function_results;
  llvm::SetVector<func::FuncOp> worklist;
  for (auto func_op : module.getOps<func::FuncOp>()) {
    worklist.insert(func_op);
    per_function_results.try_emplace(
        func_op, func_op, resource_alias_analysis.GetAnalysisForFunc(func_op));
  }

  Value input = func_op.getArgument(0);
  Value output_init_state = func_op.getArgument(1);
  Value hidden_init_state = func_op.getArgument(2);
  Value weight_kernel = func_op.getArgument(3);
  Value recurrent_kernel = func_op.getArgument(4);
  Value bias = func_op.getArgument(5);

  // The func op should have 5 outputs.
  if (func_op.getNumResults() != 5) return failure();

                               TF::kStablehloEntryFunctionAttrName)
                           .getValue();
  func::FuncOp func_op = symbol_table.lookup<func::FuncOp>(function_name);

  // We should not unwrap if the function is not from
  // ReplaceStablehloOpsInMainFunctionWithXlaCallModuleOpsPass.
  if (!func_op->hasAttr(TF::kFromXlaCallModuleAttrName)) {
    return;
  }

  MLIRContext* context = call_op.getContext();

    TensorArraySideEffectAnalysis tensor_array_side_effect_analysis(module);

    for (auto func_op : module.getOps<mlir::func::FuncOp>()) {
      func_op.emitRemark() << "HasAtMostTensorArrayEffect: "
                           << tensor_array_side_effect_analysis
                                  .HasAtMostTensorArrayEffect(func_op);
    }
  }
};

mlir::PassRegistration<TestTensorArraySideEffectAnalysis> pass;

// new goroutine.
//
// Func.Release must be called to free up resources when the function will not be invoked any more.
func FuncOf(fn func(this Value, args []Value) any) Func {
	funcsMu.Lock()
	id := nextFuncID
	nextFuncID++
	funcs[id] = fn
	funcsMu.Unlock()
	return Func{
		id:    id,
		Value: jsGo.Call("_makeFuncWrapper", id),
	}
}