%2 = "tf.Const"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>
  %3 = "tf.Const"() {value = dense<2> : tensor<i32>} : () -> tensor<i32>
  %4 = "tf.StackV2"(%3) {elem_type = f32, stack_name = "s"} : (tensor<i32>) -> tensor<!tf_type.resource>
  %5:5 = "tfl.while"(%2, %3, %2, %4, %0) ({

    maximum size needs to be a constant and 2) a push op can be found with a
    known shape, and all push ops need to have the same shape.

    A stack creation op "tf.StackV2" will be turned in to two zero-initialized
    variables, for the buffer and current size. Each push will be turned into
    ```mlir
      %old_val = "tf.ReadVariableOp"(%buffer)
      %old_size = "tf.ReadVariableOp"(%size)

  let results = (outs
    Res<TF_Tensor, [{The same tensor as the input 'elem'.}]>:$output
  );

  TF_DerivedOperandTypeAttr T = TF_DerivedOperandTypeAttr<1>;
}

def TF_StackV2Op : TF_Op<"StackV2", [TF_UniqueResourceAllocation]> {
  let summary = "A stack that produces elements in first-in last-out order.";

  let arguments = (ins