PatternRewriter &rewriter) {
  if (concat.getVal().size() < 2)
    return rewriter.notifyMatchFailure(
        concat, "Concatenate op should have at least two operands");

  auto first = concat.getVal()[0].getDefiningOp<mhlo::SliceOp>();
  auto second = concat.getVal()[1].getDefiningOp<mhlo::SliceOp>();
  if (!first || !second)

			HTTPGet: &apimirror.HTTPGetAction{
				Path: "/hello/sunnyvale",
				Port: apimirror.IntOrString{IntVal: int32(appPort)},
			},
		},
		"/app-health/hello-world/livez": &Prober{
			HTTPGet: &apimirror.HTTPGetAction{
				Port: apimirror.IntOrString{IntVal: int32(appPort)},
			},
		},
	}
	simpleTCPConfig := KubeAppProbers{
		"/app-health/hello-world/readyz": &Prober{

								Protocol: v1.ProtocolUDP,
								TargetPort: intstr.IntOrString{
									Type:   0,
									IntVal: 53,
								},
							},
							{
								Name:     "dns-tcp",
								Port:     53,
								Protocol: v1.ProtocolTCP,
								TargetPort: intstr.IntOrString{
									Type:   0,
									IntVal: 53,
								},
							},
						},
						Selector: map[string]string{

                             int* num_retvals) override {
    std::vector<TFE_TensorHandle*> outputs(*num_retvals);
    TF_Status status;
    device_.execute(tensorflow::wrap(op), num_retvals, outputs.data(), &status,
                    info_);
    if (status.status.ok()) {
      for (int i = 0; i < *num_retvals; ++i) {
        retvals[i] = tensorflow::unwrap(outputs[i]);

}

Status MlirAbstractOp::Execute(absl::Span<AbstractTensorHandle*> retvals,
                               int* num_retvals) {
  OperationState* state;
  TF_RETURN_IF_ERROR(Create(operands_, &state));
  Operation* op = function_context_->CreateOperationFromState(*state);
  *num_retvals = op->getNumResults();
  for (int i = 0; i < *num_retvals; i++)
    retvals[i] = new MlirTensor(op->getResult(i));
  return absl::OkStatus();
}

    return op.emitOpError(
        llvm::formatv(kBadArrayAttrLengthMsg, tensorflow::kOutputShardingAttr,
                      op.getNumResults(), output_shardings.size()));

  // Set retvals metadata in proto.
  for (auto output_sharding_and_idx : llvm::enumerate(output_shardings))
    if (failed(SetOpSharding(op, output_sharding_and_idx.value(),
                             tensorflow::kOutputShardingAttr,

		panic("variable value type must be a pointer")
	}
	defVal := reflect.ValueOf(val)
	if defVal.Kind() == reflect.Ptr {
		defVal = defVal.Elem()
	}
	if defVal.Type() != ptrVal.Type().Elem() {
		panic(fmt.Sprintf("default type does not match variable type: %v != %v", defVal.Type(), ptrVal.Type().Elem()))
	}
	ptrVal.Elem().Set(defVal)
	return textValue{p}
}

func (v textValue) Set(s string) error {

    func.func @main(%arg0: tensor<3x3x10xbf16>, %arg1: tensor<3xi32>) -> tensor<1x?x4xbf16> attributes {allow_soft_placement = false, tf.entry_function = {control_outputs = "", inputs = "_arg0,_arg1,_arg2", outputs = "_retval0"}} {
      %cst = "tf.Const"() {value = dense<[1, -1, 4]> : tensor<3xi32>} : () -> tensor<3xi32>

	for _, e := range envs {
		if !shouldExtract(e, prefix) {
			continue
		}
		v := e[metaPrefixLen:]
		if !isEnvVar(v) {
			continue
		}
		metaKey, metaVal := parseEnvVar(v)
		set(meta, metaKey, metaVal)
	}
}

func shouldExtract(envVar, prefix string) bool {
	return strings.HasPrefix(envVar, prefix)
}

func isEnvVar(str string) bool {
	return strings.Contains(str, "=")

// NewReferencedResourceNotFound returns a new diag.Message based on ReferencedResourceNotFound.
func NewReferencedResourceNotFound(r *resource.Instance, reftype string, refval string) diag.Message {
	return diag.NewMessage(
		ReferencedResourceNotFound,
		r,
		reftype,
		refval,
	)
}

// NewNamespaceNotInjected returns a new diag.Message based on NamespaceNotInjected.