if (!name.empty())
    node_def->set_name(std::string(ParseTensorName(name.str()).node()));
  else
    node_def->set_name(
        std::string(op_to_name_.GetUniqueName(func.getName().str())));

  node_def->set_op(FunctionLibraryDefinition::kArgOp);

  mlir::TensorType arg_type = mlir::cast<mlir::TensorType>(arg.getType());
  if (auto resource_type =
          mlir::dyn_cast<mlir::TF::ResourceType>(arg_type.getElementType())) {

  if (!name.empty())
    node_def->set_name(std::string(ParseTensorName(name.str()).node()));
  else
    node_def->set_name(
        std::string(op_to_name_.GetUniqueName(func.getName().str())));

  node_def->set_op(FunctionLibraryDefinition::kArgOp);

  mlir::TensorType arg_type = mlir::cast<mlir::TensorType>(arg.getType());
  if (auto resource_type =
          mlir::dyn_cast<mlir::TF::ResourceType>(arg_type.getElementType())) {

    const absl::flat_hash_map<absl::string_view, int>& histogram) {
  for (const auto& pair : histogram) {
    XlaAutoClusteringSummary::OpAndCount* new_entry = result->Add();
    new_entry->set_op(std::string(pair.first));
    new_entry->set_count(pair.second);
  }

  absl::c_sort(*result, [](const XlaAutoClusteringSummary::OpAndCount& a,
                           const XlaAutoClusteringSummary::OpAndCount& b) {

    TF_ASSIGN_OR_RETURN(auto tf_name,
                        GetTensorFlowOpName(inst->getName().getStringRef()));
    op_name.append(tf_name);
  }

  node_def->set_name(name.str());
  node_def->set_op(std::string(op_name.str()));

  // Update NodeDef constructed out of an MLIR Case/If/While op to map it to
  // either TensorFlow StatelessX or X op depending on the additional attribute.

    }

    class MyAspect implements ModelSchemaAspect {}

    @Managed
    static abstract class MyTypeOfAspect {
        abstract String getProp()

        abstract void setProp(String prop)

        String getCalculatedProp() {
            return "calc"
        }
    }

    def "aspects can be extracted"() {
        def aspect = new MyAspect()

    response.use {
      assertEquals(200, response.code)
      assertEquals(Protocol.HTTP_2, response.protocol)
    }

    // Only logs to the test logger above
    // Will fail if "adb shell setprop log.tag.okhttp.Http2 DEBUG" is called
    assertEquals(setOf(OkHttpTest::class.java.name), testHandler.calls.keys)
  }

  fun testCachedRequest() {
    enableTls()

  const int num_inputs = input_shapes->num_items;
  NodeDef node_def;
  tensorflow::ImmediateExecutionOperation* op = tensorflow::unwrap(tfe_op);
  node_def.set_name(op->Name());
  node_def.set_op(op->Name());
  for (int i = 0; i < num_inputs; ++i) {
    node_def.add_input("dummy_input");
  }
  OperationFromInterface(op)->Attrs().FillAttrValueMap(node_def.mutable_attr());

	if n.TypeWord == nil || n.TypeWord.Op() == ir.OADDR && n.TypeWord.(*ir.AddrExpr).X.Op() == ir.OLINKSYMOFFSET {
		// Fixed (not loaded from a dictionary) type.
		ta := ir.NewTypeAssertExpr(base.Pos, c, toType)
		ta.SetOp(ir.ODOTTYPE2)
		// Allocate a descriptor for this conversion to pass to the runtime.
		ta.Descriptor = makeTypeAssertDescriptor(toType, true)
		rhs = ta
	} else {

	// same type as the variable on the lhs.
	if ok := n.Lhs[1]; !ir.IsBlank(ok) && ok.Type().IsBoolean() {
		call.Type().Field(1).Type = ok.Type()
	}
	n.Rhs = []ir.Node{call}
	n.SetOp(ir.OAS2FUNC)

	// don't generate a = *var if a is _
	if ir.IsBlank(a) {
		return walkExpr(typecheck.Stmt(n), init)
	}

	var_ := typecheck.TempAt(base.Pos, ir.CurFunc, types.NewPtr(t.Elem()))

	// because walkExpr will lower the string
	// conversion into a runtime call.
	// See issue 24937 for more discussion.
	if cond.Op() == ir.OBYTES2STR && allCaseExprsAreSideEffectFree(sw) {
		cond := cond.(*ir.ConvExpr)
		cond.SetOp(ir.OBYTES2STRTMP)
	}

	cond = walkExpr(cond, sw.PtrInit())
	if cond.Op() != ir.OLITERAL && cond.Op() != ir.ONIL {
		cond = copyExpr(cond, cond.Type(), &sw.Compiled)
	}

	base.Pos = lno

	s := exprSwitch{