func.return %1 : tensor<1x3xf32>
}

func.func @main(%arg0: tensor<1x3xf32>) -> tensor<1x3xf32> attributes {tf.entry_function = {control_outputs = "", inputs = "input:0", outputs = "output:0"}} {
  %0 = "tf.VarHandleOp"() {device = "/device:CPU:0", container = "", shared_name = "variable"} : () -> tensor<!tf_type.resource<tensor<1x3xf32>>>

}

// CHECK-LABEL: func @main
func.func @main(%arg0: tensor<1x3xf32>) -> tensor<*xf32> attributes {tf.entry_function = {control_outputs = "", inputs = "input:0", outputs = "batch/BatchFunction:0"}} {
  %0 = "tf.VarHandleOp"() {device = "/device:CPU:0", container = "", shared_name = "variable"} : () -> tensor<!tf_type.resource<tensor<1x3xf32>>>
  // CHECK: tfrt_fallback_async.batch_function device("/device:CPU:0") @batched_function

  func.func @main_func() -> (tensor<*xf32> {tf_saved_model.index_path = ["a"]}) attributes {tf_saved_model.exported_names = ["main_func"]} {
    %0 = tf_executor.graph {
      %outputs_0, %control_0 = tf_executor.island wraps "tf.VarHandleOp"() {container = "", shared_name = ""} : () -> tensor<!tf_type.resource<tensor<501000x128xf32>>>

// 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>) -> ()

  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.

//

  static const char* kOpsToSkip[] = {
      "IdentityOp",
      "NoOp",              // b/174596063
      "OptionalHasValue",  // b/173136483
      "OptionalGetValue",  // b/173136483
      "VarHandleOp",       // b/176819198
  };
  for (const char* skip : kOpsToSkip) {
    if (absl::StartsWith(orig_op->op_name(), skip)) return absl::OkStatus();
  }

  tf_core_op_expansion_node_counter->GetCell()->IncrementBy(1);