code: def on_after_backward(self):
      assert self.layer.weight.grad.dtype == torch.float64
  
imports: import pickle
  from unittest.mock import MagicMock
  import pytest
  import torch
  from lightning.pytorch import Trainer
  from lightning.pytorch.demos.boring_classes import BoringModel, RandomDataset
  from lightning.pytorch.plugins.precision.double import DoublePrecision
  from torch.utils.data import DataLoader, Dataset
  from tests_pytorch.helpers.runif import RunIf
label_desc: Use image node_id 7 for calling the DoublePrecisionBoringModel obj's underlying member method code with example usage: obj.on_after_backward() without return types