code: def on_fit_start(self):
      # when the default floating point type is float64 the default complex type is complex128, as long as it is
      # initialized under the precision context manager, because `model.to(double)` will not convert properly
      assert self.complex_buffer_wrong.dtype == torch.complex64
      assert self.complex_buffer_right.dtype == torch.complex128
      # this hook is not wrapped
      assert torch.tensor([1.2, 3.4j]).dtype == torch.complex64
  
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 3 for calling the DoublePrecisionBoringModelComplexBuffer obj's underlying member method code with example usage: obj.on_fit_start() without return types