namespace {
Node* MakeRead(const Scope& scope, const string& id,
               Node** var_handle_op = nullptr) {
  Output var_handle =
      ops::VarHandleOp(scope.WithOpName("Var" + id), DT_FLOAT, TensorShape({}));
  Output read =
      ops::ReadVariableOp(scope.WithOpName("Read" + id), var_handle, DT_FLOAT);
  if (var_handle_op) {
    *var_handle_op = var_handle.node();
  }
  return read.node();
}

shared_name: the name by which this variable is referred to.
dtype and shape: attributes representing the data type and shape held in the
  variable.

Example:
    resource_variable_ops.var_handle_op(
          dtype=dtypes.int32, shape=[8, 16], container="foo", shared_name="bar")
  returns a handle for a variable with name "bar" in container "foo", and the
  variable holds a tensor of shape [8, 16] and dtype int32.
  }];

    // CHECK-DAG: [[VAR_HANDLE:%.*]] = "tf.VarHandleOp"
    // CHECK-DAG: [[MG_HANDLE:%.*]] = "tf.VarHandleOp"
    // CHECK-DAG: [[MS_HANDLE:%.*]] = "tf.VarHandleOp"
    // CHECK-DAG: [[MOM_HANDLE:%.*]] = "tf.VarHandleOp"
    %0 = "tf.VarHandleOp"() {container = "c", shared_name = "v"} : () -> tensor<*x!tf_type.resource<tensor<f32>>>

// CHECK-LABEL: func @cluster_with_nested_loop
func.func @cluster_with_nested_loop() -> () {
  // CHECK: %[[VH:.*]] = "tf.VarHandleOp"()
  %0 = "tf.VarHandleOp"() {container = "c", shared_name = "v"} : () -> tensor<*x!tf_type.resource<tensor<f32>>>
  // CHECK: %[[VH_UNUSED:.*]] = "tf.VarHandleOp"()
  %1 = "tf.VarHandleOp"() {container = "c", shared_name = "v2"} : () -> tensor<*x!tf_type.resource<tensor<f32>>>

func.func @resource_before_cluster() {
  // CHECK-NEXT: [[CONST:%.*]] = "tf.Const"
  %0 = "tf.Const"() {value = dense<1.000000e+00> : tensor<f32>} : () -> tensor<f32>

  // CHECK-NEXT: [[UNUSED_RESOURCE:%.*]] = "tf.VarHandleOp"
  // CHECK-NEXT: "tf.AssignAddVariableOp"([[UNUSED_RESOURCE]], [[CONST]])

  // CHECK:      "tf_device.cluster"
  // CHECK-NEXT:   "tf.NoOp"
  // CHECK-NEXT:   tf_device.return

  TFE_DeleteContextOptions(opts);

  TFE_Op* var_op = TFE_NewOp(ctx, "VarHandleOp", status);
  TFE_OpSetAttrType(var_op, "dtype", TF_INT64);
  TFE_OpSetAttrShape(var_op, "shape", {}, 0, status);
  const TFE_OpAttrs* attributes = TFE_OpGetAttrs(var_op);

  TFE_Op* copy_op = TFE_NewOp(ctx, "VarHandleOp", status);
  TFE_OpSetAttrType(copy_op, "dtype", TF_FLOAT);
  TFE_OpAddAttrs(copy_op, attributes);

// Tests loop with two resource types, one of them being unique per iteration.
//
// Similar to above test but with one additional resource that is not unique per
// iteration (created by `tf.VarHandleOp`).

func.func @mixed_unique_resource_chain(%arg0: tensor<i32>, %arg1: tensor<f32>) {
  tf_executor.graph {

   if the variable is read only.

   For example, the following pseudo MLIR code (types are left out for
   brevity)
   ```mlir
     func.func @hoist_loop_invariant(%arg0, %arg1) {
%var = "tf.VarHandleOp"() {container="", shared_name="var_name", device = "/device:CPU:0"}
       %results:2 = "tf.WhileRegion"(%arg0, %arg1) ({
       ^bb0(%arg2, %arg3):
         %0 = "tf.OpA"() {is_stateless = true}
         "tf.Yield"(%0)

  %outputs_8  =  "tf.Const"() {device = "/job:localhost/replica:0/task:0/device:CPU:0", value = dense<1> : tensor<i32>} : () -> tensor<i32>
  // CHECK: [[elems:%.*]] = "tf.VarHandleOp"
  %outputs_10  =  "tf.VarHandleOp"() {_xla_inferred_shapes = [#tf_type.shape<>], allowed_devices = [], container = "", device = "/job:localhost/replica:0/task:0/device:CPU:0", shared_name = "w"} : () -> tensor<!tf_type.resource<tensor<3x1xf32>>>

    if (std::optional<absl::string_view> cluster_name =
            GetXlaClusterForNode(*n)) {
      n->set_name(absl::StrCat(*cluster_name, "/", n->name()));
    } else if (n->type_string() == "VarHandleOp") {
      n->set_name(absl::StrCat("varhandle/", n->name()));
    } else {
      // There is room for improvement here.  In particular, it may help to