for (auto user : result.getUsers()) {
      if (dyn_cast_or_null<TF::DumpTensorOp>(user)) return failure();
    }

    const FlatSymbolRefAttr f_attr = GetFuncAttr(op);
    if (!f_attr.getValue().starts_with(kCompositeFuncPrefix)) return failure();
    return success();
  }

  void rewrite(LiftedOpT op, PatternRewriter &rewriter) const override {
    // Only support ops with 1 results

                                PatternRewriter& rewriter) const override {
    StringRef function_name =
        mlir::cast<FlatSymbolRefAttr>(call_op.getFAttr()).getValue();
    if (!function_name.starts_with("composite_") ||
        !call_op->hasAttr(kQuantTraitAttrName)) {
      return failure();
    }

    absl::Status check_status;
    // TODO(b/270906404): Support weight-only gather for uniform quantized opset

    mlir::NamedAttrList list;
    for (mlir::NamedAttribute attr : std::get<1>(it).getValue()) {
      // Skip if the attribute has "tfg" prefix.
      if (attr.getName().getValue().starts_with("tfg")) continue;
      list.append(attr);
    }
    output_attrs.push_back(list.getDictionary(context));
  }
  return mlir::success();
}

static mlir::LogicalResult ReformatOpAttributes(

    if (!call_op->hasAttr(kQuantTraitAttrName)) {
      return false;
    }

    const auto f_attr = call_op.getFAttr().dyn_cast<FlatSymbolRefAttr>();
    if (!f_attr || !f_attr.getValue().starts_with("composite_")) {
      return false;
    }

    bool has_quantized_types = false;
    for (Value input : call_op.getArgs()) {
      if (auto type = input.getType().dyn_cast<TensorType>()) {

}

}  // namespace

bool mlir::IsStablehloOp(const tflite::OperatorCodeT& op_code) {
  llvm::StringRef op_name(
      tflite::EnumNameBuiltinOperator(tflite::GetBuiltinCode(&op_code)));
  return op_name.starts_with("STABLEHLO_");
}

std::string mlir::GetMlirOpNameFromOpCode(
    const tflite::OperatorCodeT& op_code) {
  auto builtin_code = tflite::GetBuiltinCode(&op_code);
  if (builtin_code == tflite::BuiltinOperator_IF) {

};

}  // namespace

// Checks if an op calls a composite function and all the inputs and outputs are
// quantized.
bool IsQuantizedCompositeFunction(func::CallOp call_op) {
  if (!call_op.getCallee().starts_with("quantized_")) {
    return false;
  }

  bool has_quantized_types = false;
  for (Value operand : call_op.getOperands()) {
    if (const TensorType type = mlir::dyn_cast<TensorType>(operand.getType())) {

  // examples quite a bit nicer.
  for (auto func :
       llvm::make_early_inc_range(module->getOps<mlir::func::FuncOp>())) {
    if (func.getName().starts_with("__inference__traced_save_") ||
        func.getName().starts_with("__inference__traced_restore_") ||
        func.getName().starts_with("__inference_signature_wrapper_")) {
      func.erase();
    }
  }

  // Diagnose SavedFunction's with multiple input signatures.

        assertEquals(5, values.length);
        assertEquals("url:test", values[0]);
        assertTrue(values[1].startsWith("time:"));
        assertTrue(values[2].startsWith("done:"));
        assertTrue(values[3].startsWith("aaa:"));
        assertTrue(values[4].startsWith("bbb:"));

        localLogMsg.remove();
        crawlerStatsHelper.done(key);
        assertNull(localLogMsg.get());
    }

          message.startsWith("System property ") -> Type.Setup
          message.startsWith("Reload ") -> Type.Setup
          message == "No session to resume." -> Type.Handshake
          message.startsWith("Consuming ") -> Type.Handshake
          message.startsWith("Produced ") -> Type.Handshake
          message.startsWith("Negotiated ") -> Type.Handshake
          message.startsWith("Found resumable session") -> Type.Handshake

        // JDK calls
        return className.startsWith("java.") ||
            className.startsWith("jdk.") ||
            className.startsWith("sun.") ||
            className.startsWith("com.sun.") ||
            // Groovy calls
            className.startsWith("groovy.lang") ||
            className.startsWith("org.codehaus.groovy.") ||
            isGradleCall(className);
    }