compile {
            outgoing {
                variants {
                    var1 {
                        artifact file('a1.jar')
                        attributes.attribute(buildType, 'profile')
                        attributes.attribute(flavor, 'bland')
                    }
                    var2 {
                        artifact file('a2.jar')

      CreateVariable(ctx_0, 1.2, remote_device, /*variable_name=*/"var2");

  // 2. Wait for `var2` to be created and initialized on the worker.
  TF_Status* status = TF_NewStatus();
  TFE_ContextAsyncWait(ctx_0, status);
  EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TF_DeleteStatus(status);

  // 3. Read `var_2` using `ctx_1`. This read should succeed since `ctx_1` was

    // CHECK: %[[VAR0:.*]] = "tf.VarHandleOp"()
    %0 = "tf.VarHandleOp"() {container = "", shared_name = "var0"} : () -> tensor<!tf_type.resource<tensor<!tf_type.string>>>

    // CHECK: "tf.AssignVariableOp"(%[[VAR0]], %[[ASSET0]])
    "tf.AssignVariableOp"(%0, %arg0) : (tensor<!tf_type.resource<tensor<!tf_type.string>>>, tensor<!tf_type.string>) -> ()

    // CHECK: %[[VAR1:.*]] = "tf.VarHandleOp"()

    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 = ...

  // CHECK-SAME: tf_saved_model.initializer_type = "restore_op"
  // CHECK: %[[VAR:.*]] = "tf.VarHandleOp"()
  // CHECK-SAME: "var1"
  // CHECK: %[[CST:.*]] = arith.constant dense<> : tensor<0xf32>
  // CHECK: "tf.AssignVariableOp"(%[[VAR]], %[[CST]])
}


// -----

// Test invalid tensor
// expected-error@below{{Can't find variable var5 in session}}
module attributes {tf_saved_model.semantics, tf_saved_model.under_construction} {

from tensorflow.compiler.mlir.tensorflow.tests.tf_saved_model import common_v1

# CHECK: "tf_saved_model.global_tensor"() <{is_mutable, sym_name = "[[VAR0:[a-zA-Z_0-9]+]]", type = tensor<5x3xf32>, value = {{.*}} : tensor<5x3xf32>}> : () -> ()
# CHECK: "tf_saved_model.global_tensor"() <{is_mutable, sym_name = "[[VAR1:[a-zA-Z_0-9]+]]", type = tensor<3x5xf32>, value = {{.*}} : tensor<3x5xf32>}> : () -> ()
# CHECK:      func {{@[a-zA-Z_0-9]+}}(

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 {

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>

		{JSONSchemaPropsOrBoolHolder{JSPoBOmitEmpty: &JSONSchemaPropsOrBool{Schema: &JSONSchemaProps{Type: "string"}}}, `{"val1":false,"val2":{"type":"string"}}`},
		{JSONSchemaPropsOrBoolHolder{JSPoBOmitEmpty: &JSONSchemaPropsOrBool{}}, `{"val1":false,"val2":false}`},
		{JSONSchemaPropsOrBoolHolder{JSPoBOmitEmpty: &JSONSchemaPropsOrBool{Allows: true}}, `{"val1":false,"val2":true}`},
	}

VariableInfo::VariableInfo(
    int index, absl::string_view name, Var* var,
    const std::optional<ManagedStackTrace>& definition_stack_trace)
    : index_(index),
      name_(name),
      var_(var),
      definition_stack_trace_(definition_stack_trace) {}

VariableInfo::VariableInfo(VariableInfo&& other)
    : index_(other.index_),
      var_(other.var_),
      definition_stack_trace_(other.definition_stack_trace_),