// inlined.

func.func private @simple_callee_var() -> tensor<2xi32>  {
  %cst = "tf.Const"() { value = dense<2> : tensor<2xi32> } : () -> tensor<2xi32>
  %0 = "tf.VarHandleOp"() {container = "c", shared_name = "v"} : () -> tensor<!tf_type.resource<tensor<2xi32>>>
  "tf.AssignVariableOp"(%0, %cst) {device = ""} : (tensor<!tf_type.resource<tensor<2xi32>>>, tensor<2xi32>) -> ()

// CHECK: }

func.func @unknown_side_effecting_op(%arg0: tensor<32xf32>) -> () {
  tf_executor.graph {
    %island = tf_executor.island {
      %vh0 = "tf.VarHandleOp"() {container = "c", shared_name = "v0"} : () -> tensor<*x!tf_type.resource<tensor<32xf32>>>
      %vh1 = "tf.VarHandleOp"() {container = "c", shared_name = "v1"} : () -> tensor<*x!tf_type.resource<tensor<32xf32>>>

func.func @unknown_side_effecting_op(%arg0: tensor<32xf32>) {
  tf_executor.graph {
    %outputs, %control = tf_executor.island wraps "tf.VarHandleOp"() {container = "c", shared_name = "v0"} : () -> tensor<*x!tf_type.resource<tensor<32xf32>>>
    %outputs_0, %control_1 = tf_executor.island wraps "tf.VarHandleOp"() {container = "c", shared_name = "v1"} : () -> tensor<*x!tf_type.resource<tensor<32xf32>>>

  bool unconditionally_use_set_output_shapes = false;

  // Apply default attributes from the op definition to the loaded op.
  bool add_default_attributes = true;

  // If set, promote tf.VarHandleOp to resource arguments for all functions.
  bool lift_variables = true;

  // Keeps the variables in initializers before lifting variables (when
  // `lift_variables == true`) or newly adding variable initialization patterns

  auto* operation = resource.getDefiningOp();
  if (operation && isa<TF::VarHandleOp>(operation)) {
    mutable_variables_.insert(GetResourceKey(operation));
  }
}

bool ResourceAnalyzer::IsPotentiallyWritten(Value resource) const {
  assert(IsResource(resource));
  auto* operation = resource.getDefiningOp();
  if (operation && isa<TF::VarHandleOp>(operation))
    return mutable_variables_.contains(GetResourceKey(operation));

#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/platform/status.h"

namespace tensorflow {
namespace internal {

// Executes a VarHandleOp using `ctx`, and fills `handle` with the DT_RESOURCE
// TensorHandle associated with the variable. This is equivalent to creating an
// unitialized TF2 tf.Variable.

      Operation *op = *latticeElement->getValue().ops.begin();
      GlobalTensorOp globalTensor = llvm::dyn_cast<GlobalTensorOp>(op);

      if (!globalTensor)
        continue;  // happens if the name is e.g. in a VarHandleOp.

      if (globalTensor.getIsMutable()) {
        freezeable[val] = false;
        continue;
      }

      freezeable[val] = true;

      // Verify users are supported kind.

//

// Creates the per-device variables that represent the formatting state of each
// device.
llvm::SmallVector<TF::VarHandleOp, 4> CreateStateVars(
    const llvm::SmallDenseMap<llvm::StringRef, llvm::SmallVector<StringRef, 4>>&
        devices,
    Location loc, RankedTensorType key_type, OpBuilder* builder) {
  llvm::SmallVector<TF::VarHandleOp, 4> state_vars;

  // TODO(b/148913020): Remove this constraint once model parallelism is
  // supported.

                               result);
}

Node* MakeWrite(const Scope& scope, Output value_to_write, const string& id) {
  Output var_handle = ops::VarHandleOp(scope.WithOpName("Var_" + id), DT_FLOAT,
                                       TensorShape({}));
  ops::AssignVariableOp assign_op(scope.WithOpName("Assignee_" + id),