}
  };

  while (!worklist.empty()) {
    tensorlist = worklist.top();
    worklist.pop();

    // TensorLists are semantically immutable. For example, TensorListSetItem
    // takes a TensorList as input and produces a TensorList as output. So to
    // traverse modifications to TensorList and verify that all elements written

  let summary =
    "Replicate TensorList init ops for correct shape assignments in shape inference";

  let description = [{
    If we pass same TensorList to a while op as multiple arguments or just use
    the same TensorList at multiple places and assign different
    TensorListSetItem to elements of TensorList, the shape inference is then

    // CHECK: %[[CAST:.*]] = "tf.Cast"(%[[ADD]]) {{.*}} : (tensor<10xf32>) -> tensor<*xf32>
    // CHECK: %[[TENSOR_CAST:.*]] = tensor.cast %[[CAST]] : tensor<*xf32> to tensor<?xf32>
    // CHECK: return %[[TENSOR_CAST]] : tensor<?xf32>
    %0 = "tf.Mul"(%arg0, %arg1) : (tensor<1xf32>, tensor<10xf32>) -> tensor<*xf32>
    %1 = "tf.Add"(%0, %0) : (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>

namespace {
// If the input of TensorListGetItemOp is TensorListFromTensorOp and the
// TensorListFromTensorOp is only used by TensorListGetItemOp (not modified by
// other TensorList ops), we can convert it to a GatherOp.
class ConvertTensorListGetItemOpOfTensorListFromTensorOpToGather
    : public OpRewritePattern<TensorListGetItemOp> {
  using OpRewritePattern<TensorListGetItemOp>::OpRewritePattern;

    shape_inference::InferenceContext* shape_context =
        shape_refiner_->GetContext(&node);
    return shape_ic(shape_context);
  }

  // Treat TensorList init ops specially here as the op requires knowing its
  // element dtype.
  // TODO(jpienaar): Reconsider post refactoring shape functions.
  if (node.type_string() == "TensorListReserve" ||