data := []byte(`3.14`)
	b.RunParallel(func(pb *testing.PB) {
		var f float64
		for pb.Next() {
			if err := Unmarshal(data, &f); err != nil {
				b.Fatalf("Unmarshal error: %v", err)
			}
		}
	})
}

func BenchmarkUnmarshalInt64(b *testing.B) {
	b.ReportAllocs()
	data := []byte(`3`)
	b.RunParallel(func(pb *testing.PB) {
		var x int64
		for pb.Next() {
			if err := Unmarshal(data, &x); err != nil {

        "test_graph_tfmatmulandadd.pb",
        "test_graph_tfsplits.pb",
        "test_graph_tftop_k.pb",
        "test_graph_tfvariable.pb",
        "test_graph_tfvariable_readonly.pb",
        "test_graph_tfvariable_sequential_updates.pb",
    ],
    # Set CUDA_VISIBLE_DEVICES='' to prevent the code we launch from using any
    # GPUs which might be present.  This is important because builds may run

#include "tensorflow/core/framework/graph.pb.h"
#include "tensorflow/core/platform/path.h"
#include "tensorflow/core/protobuf/meta_graph.pb.h"
#include "tensorflow/core/protobuf/saved_object_graph.pb.h"
#include "tensorflow/tools/proto_splitter/cc/util.h"
#include "tensorflow/tools/proto_splitter/chunk.pb.h"
#include "tensorflow/tools/proto_splitter/testdata/test_message.pb.h"
#include "tsl/lib/core/status_test_util.h"

		t.Errorf("reqs.Value() = %v, want -2", i)
	}
}

func BenchmarkIntAdd(b *testing.B) {
	var v Int

	b.RunParallel(func(pb *testing.PB) {
		for pb.Next() {
			v.Add(1)
		}
	})
}

func BenchmarkIntSet(b *testing.B) {
	var v Int

	b.RunParallel(func(pb *testing.PB) {
		for pb.Next() {
			v.Set(1)
		}
	})
}

func TestFloat(t *testing.T) {
	RemoveAll()
	reqs := NewFloat("requests-float")

// IntPerLayer-DAG: "tf.DumpTensor"(%[[output1_quantized]]) <{enabled = true, file_name = "quantized_tensor_data.pb", func_name = "conv", log_dir_path = "/tmp/dumps/composite_conv2d_with_bias_and_relu6_fn_1", node_name = "Conv2D_1"}> : (tensor<*xf32>) -> ()

	// but also not used by Istio at all.
	if pb, ok := s.(protoreflect.ProtoMessage); ok {
		return protoconv.MessageToAnyWithError(pb)
	}

	// gogo protobuf
	if pb, ok := s.(gogoproto.Message); ok {
		gogoany, err := gogotypes.MarshalAny(pb)
		if err != nil {
			return nil, err
		}
		return &anypb.Any{
			TypeUrl: gogoany.TypeUrl,

		benchmark(b, func(pb *testing.PB) {
			for pb.Next() {
				NewTimer(1 * Second).Stop()
			}
		})
	})
	b.Run("impl=func", func(b *testing.B) {
		benchmark(b, func(pb *testing.PB) {
			for pb.Next() {
				newTimerFunc(1 * Second).Stop()
			}
		})
	})
}

func BenchmarkSimultaneousAfterFunc1000(b *testing.B) {
	benchmark(b, func(pb *testing.PB) {
		for pb.Next() {

// total samples collected.
func (p *Profile) Merge(pb *Profile, r float64) error {
	if err := p.Compatible(pb); err != nil {
		return err
	}

	pb = pb.Copy()

	// Keep the largest of the two periods.
	if pb.Period > p.Period {
		p.Period = pb.Period
	}

	p.DurationNanos += pb.DurationNanos

	p.Mapping = append(p.Mapping, pb.Mapping...)
	for i, m := range p.Mapping {
		m.ID = uint64(i + 1)

}

TEST(CreateExportedModelTest, CreateExportedModelWithAddedAssetFileDefs) {
  AssetFileDef asset1;
  ASSERT_TRUE(
      TextFormat::ParseFromString(R"pb(filename: "fname1")pb", &asset1));

  AssetFileDef asset2;
  ASSERT_TRUE(
      TextFormat::ParseFromString(R"pb(filename: "fname2")pb", &asset2));

  const ExportedModel exported_model = CreateExportedModelFromGraphDef(
      GraphDef(), /*init_node_name=*/"", /*checkpoint_dir=*/"",

// WholeModel-DAG: "tf.DumpTensor"(%[[matmul0_q]]) <{enabled = true, file_name = "unquantized_tensor_data.pb", func_name = "composite_dot_general_with_bias_and_relu6_dynamic_fn_2", log_dir_path = "/tmp/dumps/composite_dot_general_with_bias_and_relu6_dynamic_fn_2", node_name = "_empty_node"}> : (tensor<?x2xf32>) -> ()