// CHECK:           %[[VAL_5:.*]] = tfl.add %[[VAL_0]], %[[VAL_1]] {fused_activation_function = "RELU6", tac.device = "GPU", tac.inference_type = "FLOAT"} : tensor<1xf32>
// CHECK:           %[[VAL_6:.*]] = tfl.mul %[[VAL_5]], %[[VAL_2]] {fused_activation_function = "RELU6", tac.device = "GPU", tac.inference_type = "FLOAT"} : tensor<1xf32>

		// inf
		{"Inf", float32(math.Inf(+1)), Exact},
	} {
		for i := 0; i < 2; i++ {
			// test both signs
			tx, tout, tacc := test.x, test.out, test.acc
			if i != 0 {
				tx = "-" + tx
				tout = -tout
				tacc = -tacc
			}

			// conversion should match strconv where syntax is agreeable
			if f, err := strconv.ParseFloat(tx, 32); err == nil && !alike32(float32(f), tout) {

        "//tensorflow/lite/kernels:builtin_ops",
        "//tensorflow/lite/kernels:reference_ops",
        "//tensorflow/lite/schema:schema_fbs",
        "//tensorflow/lite/toco/logging:conversion_log_util",
        "//tensorflow/lite/toco/logging:toco_conversion_log_proto_cc",
        "//tensorflow/lite/toco:model_flags_proto_cc",
        "//tensorflow/lite/toco:toco_convert",
        "//tensorflow/lite/toco:toco_flags_proto_cc",

	// corresponding to reachable, Go-generated, user-visible
	// global variables. For each global of this sort, locate
	// the corresponding compiler-generated DIE symbol and tack
	// it onto the list associated with the unit.
	// Also looks for dictionary symbols and generates DIE symbols for each
	// type they reference.
	for idx := loader.Sym(1); idx < loader.Sym(d.ldr.NDef()); idx++ {

u,lim?moc?oi,pj,su,ten?ude?v&og?t,???m!.&drp?gro?lim?m&o&c?n??t??oc?ude?vog??pk??n!.&dtl,eman?gro?hcs?i!bom??l&im?oc,?m&oc!.topsgolb,?rif,?neg,ogn,ten?ude?vog??aw?i!b!mulp??car?d&art?dew??h&sif?tolc??k&iv?oo&b?c???ls?n&aelc?iart??p!pohs??re&enigne?tac??t&ad?ekram?hgil?lusnoc?neg?ov?soh!.tfarcnepo,??vi&g?l???o!s??u&rehcisrev?smas?tarebsnegömrev???o&d?lb?og!.&duolc,etalsnart,???r&2n084qlj--nx?ebmoolb?o!.&77ndc.c:sr,,a&remacytirucesym,t&neimip,sivretla,?z,?bew-llams,d&ab-yrev-si,e&sufnocsim,vas-si,...

u,lim?moc?oi,pj,su,ten?ude?v&og?t,???m!.&drp?gro?lim?m&o&c?n??t??oc?ude?vog??pk??n!.&dtl,eman?gro?hcs?i!bom??l&im?oc,?m&oc!.topsgolb,?rif,?neg,ogn,ten?ude?vog??aw?i!b!mulp??car?d&art?dew??h&sif?tolc??k&iv?oo&b?c???ls?n&aelc?iart??p!pohs??re&enigne?tac??t&ad?ekram?hgil?lusnoc?neg?ov?soh!.tfarcnepo,??vi&g?l???o!s??u&rehcisrev?smas?tarebsnegömrev???o&d?lb?og!.&duolc,etalsnart,???r&2n084qlj--nx?ebmoolb?o!.&77ndc.c:sr,,a&remacytirucesym,t&neimip,sivretla,?z,?bew-llams,d&ab-yrev-si,e&sufnocsim,vas-si,...

}

// HealthHandler - returns true of health
func (s *peerRESTServer) HealthHandler(w http.ResponseWriter, r *http.Request) {
	s.IsValid(w, r)
}

// VerifyBinary - verifies the downloaded binary is in-tact
func (s *peerRESTServer) VerifyBinaryHandler(w http.ResponseWriter, r *http.Request) {
	if !s.IsValid(w, r) {
		s.writeErrorResponse(w, errors.New("Invalid request"))
		return
	}

	if r.ContentLength < 0 {

      return failure();
    }

    // TensorFlow convolution op only has two inputs, while the TFLite one has
    // three, with the bias vector marked as optional. However, TOCO has a
    // dedicated pass, EnsureBiasVectors, to create default bias vectors for all
    // those missing. So we model TFLite convolution op as requiring three