def METHOD_NAME(self, apot_tensor) -> Tensor:
    orig_size = apot_tensor.data.size()
    apot_tensor_data = apot_tensor.data.flatten()
    print(apot_tensor_data)
    # map apot_to_float over tensor2quantize elements
    result_temp = np.empty(shape=apot_tensor_data.size())
    for i in range(len(apot_tensor_data)):
        new_ele = apot_to_float(apot_tensor_data[i], self.quantization_levels, self.level_indices)
        result_temp[i] = new_ele
    result = torch.from_numpy(result_temp).reshape(orig_size)
    return result