func.func @init() attributes {tf_saved_model.exported_names = ["__tf_saved_model_session_initializer_init"]} {
  %var0 = "tf.VarHandleOp"() {container = "", shared_name = "var0"} : () -> tensor<!tf_type.resource<tensor<i1>>>
  %cond = "tf.ReadVariableOp"(%var0) {device = "/CPU:0"} : (tensor<!tf_type.resource<tensor<i1>>>) -> tensor<i1>

type DurationVar struct {
	Var
}

var (
	allVars = make(map[string]Var)
	mutex   sync.Mutex
)

// VarDescriptions returns a description of this process' environment variables, sorted by name.
func VarDescriptions() []Var {
	mutex.Lock()
	sorted := make([]Var, 0, len(allVars))
	for _, v := range allVars {
		sorted = append(sorted, v)
	}
	mutex.Unlock()

	sort.Slice(sorted, func(i, j int) bool {

    tf_executor.graph {
      %out, %ctl = tf_executor.island wraps "tf.VarHandleOp"() {shared_name = "var_0"} : () -> tensor<!tf_type.resource<tensor<2xf32>>>
      %out_0, %ctl_0 = tf_executor.island wraps "tf.ReadVariableOp"(%out) : (tensor<!tf_type.resource<tensor<2xf32>>>) -> tensor<2xf32>
      %out_1, %ctl_1 = tf_executor.island wraps "tf.Const"() {value = dense<"var_0"> : tensor<1x!tf_type.string>} : () -> tensor<1x!tf_type.string>

module {
// CHECK-LABEL: @serving_default
  func.func @serving_default() -> (tensor<*xi32>) {
    // expected-remark@below {{device: "", container: "", shared_name: "var_0", is_potentially_written: false}}
    %0 = "tf.VarHandleOp"() {shared_name = "var_0"} : () -> tensor<!tf_type.resource<tensor<2xi32>>>
    %1 = "tf.BatchFunction"(%0) {
        f = @called_by_batch_func,
        operandSegmentSizes = array<i32: 1, 0>,

// Test case: Basic freezing.

module {
  // CHECK: func @main()
  func.func @main() -> tensor<0xf32> {
    %handle = "tf.VarHandleOp"() {container="", shared_name="var1", device = "/job:worker/replica:0/task:1/device:CPU:0"} : () -> tensor<!tf_type.resource<tensor<0xf32>>>
    %val = "tf.ReadVariableOp"(%handle) : (tensor<!tf_type.resource<tensor<0xf32>>>) -> tensor<0xf32>

// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.

package main

import (
	"testshared/issue47837/a"
)

func main() {
	var vara a.ImplA
	a.TheFuncWithArgA(&vara)

    if (use_resource) {
      Output var =
          ops::VarHandleOp(scope.WithOpName("var"), DataType::DT_FLOAT, {});
      read_var = ops::ReadVariableOp(
          scope.WithOpName("var/Read/ReadVariableOp"), var, DataType::DT_FLOAT);
      auto assign = ops::AssignVariableOp(scope.WithOpName("assign"), var, a);
      Output var_1 =
          ops::VarHandleOp(scope.WithOpName("var_1"), DataType::DT_FLOAT, {});

    %0 = "tf.VarHandleOp"() {_class = ["loc:@dense/kernel"], allowed_devices = [], container = "", device = "/job:worker/replica:0/task:1/device:CPU:0", shared_name = "var1"} : () -> tensor<!tf_type.resource<tensor<100x50xf32>>>
    %1 = "tf.ReadVariableOp"(%0) {device = ""} : (tensor<!tf_type.resource<tensor<100x50xf32>>>) -> tensor<100x50xf32>
    func.return %1 : tensor<100x50xf32>
  }

		// stackmap for this function. It is likely that we are looking
		// at the function prologue, assume so and hope for the best.
		pcdata = 0
	}

	// Local variables.
	size := frame.varp - frame.sp
	var minsize uintptr
	switch goarch.ArchFamily {
	case goarch.ARM64:
		minsize = sys.StackAlign
	default:
		minsize = sys.MinFrameSize
	}
	if size > minsize {
		stackid := pcdata

loop body inine):

```mlir
  %var0 = ...
  %var1 = ...
  tf.while (..., %var0, %var1) {
    tf_device.replicate ([%var0, %var1] as %rvar) {
      %compile:2 = "tf._TPUCompileMlir"()
      tf.TPUExecuteAndUpdateVariablesOp(%rvar, compile#1)
    }
  }
```

This pass will transform it into

```mlir
  %var0 = ...
  %var1 = ...
  %state_var0 = ...
  %state_var1 = ...