// CHECK-NOT:  IfrtLoadVariable
// CHECK:      "tf.VarHandleOp"
// CHECK-NEXT: "tf.AssignVariableOp"
// CHECK-NEXT: "tf.ReadVariableOp"
// CHECK-NEXT: "tf.StatefulPartitionedCall"
// CHECK-NEXT:  return 
//
module {
  func.func @serving_default() -> tensor<*xi32> {
    %cst = "tf.Const"() <{value = dense<"some_test.txt"> : tensor<!tf_type.string>}> : () -> tensor<!tf_type.string>

  let constructor = "TF::CreateHoistLoopInvariantPass()";
}

def LocalizeVarHandlesPass : Pass<"tf-localize-var-handles", "ModuleOp"> {
  let summary = "Creates VarHandleOps next to the operations that use them.";
  let description = [{
      Creates VarHandleOps right next to the operations that use them, one
      per operation.
      This is useful for transformations that only end up with a few small

// aliasing output arguments.
std::unique_ptr<OperationPass<ModuleOp>> CreatePromoteResourcesToArgsPass(
    llvm::ArrayRef<std::string> functions = {});

// Creates a pass that promotes tf.VarHandleOp to resource arguments for all
// functions.
std::unique_ptr<OperationPass<ModuleOp>> CreatePromoteVarHandlesToArgsPass();

// Creates a pass that converts readonly reference variables to the

  if (auto pop_back = dyn_cast<TF::TensorListPopBackOp>(op)) {
    return InferShapeForTensorListPopBackOp(pop_back);
  }

  if (auto var_handle_op = dyn_cast<VarHandleOp>(op)) {
    return InferShapeForVarHandleOp(var_handle_op);
  }

  if (auto xla_reduce_window_op = dyn_cast<XlaReduceWindowOp>(op)) {
    return InferShapeForXlaReduceWindowOp(xla_reduce_window_op);
  }

See `tf.scatter_nd` for more details about how to make updates to
slices.
  }];

  let arguments = (ins
    Arg<TF_ResourceTensor, [{A resource handle. Must be from a VarHandleOp.}], [TF_VariableRead, TF_VariableWrite]>:$ref,
    Arg<TF_I32OrI64Tensor, [{A Tensor. Must be one of the following types: int32, int64.
A tensor of indices into ref.}]>:$indices,