func.func @if_else(%arg0: tensor<!tf_type.resource>) -> tensor<!tf_type.resource> {
  // CHECK-NOT: "tf.StackPopV2"
  // CHECK: "tf.Slice"
  // CHECK: "tf.AssignVariableOp"(%[[EARG1:.*]],
  // CHECK-NOT: "tf.StackPopV2"
  %pop = "tf.StackPopV2"(%arg0) : (tensor<!tf_type.resource>) -> tensor<f32>
  func.return %arg0 : tensor<!tf_type.resource>
}

// -----

 Max 4
 Maximum 44
 Merge 145
 Minimum 43
 Mul 8
 NextIteration 136
 RandomUniform 14
 Range 9
 RefSwitch 166
 Reshape 2
 ScatterAdd 4
 Shape 6
 ShapeN 10
 Size 2
 Snapshot 1
 StackPopV2 314
 StackPushV2 314
 StackV2 314
 StridedSlice 12
 Sub 5
 Sum 2
 Switch 247
 TensorArrayGatherV3 8
 TensorArrayGradV3 17
 TensorArrayReadV3 9
 TensorArrayScatterV3 8
 TensorArraySizeV3 4

        // of this concession it is unsound to auto-cluster them because then
        // we'd create clusters we could not compile (because we can't constant
        // fold, say, a TensorArrayRead or a StackPopV2).  But we don't
        // auto-cluster these operations today so we're good for now.
        const XlaResourceOpInfo* op_info =
            GetResourceOpInfoForOp(node->type_string());

  let arguments = (ins
    Arg<TF_ResourceTensor, [{The handle to a stack.}], [TF_StackFree]>:$handle
  );

  let results = (outs);
}

def TF_StackPopV2Op : TF_Op<"StackPopV2", []> {
  let summary = "Pop the element at the top of the stack.";

  let arguments = (ins
    Arg<TF_ResourceTensor, [{The handle to a stack.}], [TF_StackRead, TF_StackWrite]>:$handle
  );