ProcessTensorSpec(mlir::TensorType type) {
  tensorflow::DataType dtype;
  TF_RETURN_IF_ERROR(
      ConvertScalarTypeToDataType(type.getElementType(), &dtype));

  if (!type.hasRank())
    return std::make_pair(dtype, tensorflow::PartialTensorShape());

  auto shape = type.getShape();
  llvm::SmallVector<int64_t, 4> dims;
  dims.assign(shape.begin(), shape.end());

  // TODO(b/158709815): Add support for batches with padded NMS.
  auto boxes_type =
      mlir::dyn_cast<RankedTensorType>(func_.getFunctionType().getInput(0));
  if (boxes_type == nullptr || !boxes_type.hasRank() ||
      boxes_type.getRank() != 2) {
    return func_.emitWarning() << "TFLite does not support batched input for "
                                  "non_max_suppression_padded";
  }
  return success();
}

  return shaped_type.hasStaticShape();
}

bool HasStaticShapeAtDims(Value value, const ArrayRef<int> dims) {
  auto shaped_type = mlir::dyn_cast<ShapedType>(value.getType());
  if (!shaped_type || !shaped_type.hasRank()) return false;

  for (auto dim : dims) {
    if (shaped_type.isDynamicDim(dim)) return false;
  }
  return true;
}

Type CloneTypeWithNewElementType(Type old_type, Type element_type) {

      return failure();
    }

    Value input = tf_op.getInputs();
    int quant_dim = -1;
    auto input_type = mlir::cast<ShapedType>(input.getType());
    if (PerAxis) {
      if (!input_type.hasRank()) {
        tf_op.emitError("The input should have known rank for per-channel op.");
        return failure();
      }
      // This is a special case that the quant_dim is the last dimensions.