code: def train_dataloader(self):
      dataset = RandomFloatIntDataset(32, 64)
      assert (
          dataset.float_data.dtype == torch.float32
      )  # Don't start with double data
      return DataLoader(dataset)
  
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 8 for calling the DoublePrecisionBoringModel obj's underlying member method code with example usage: obj.train_dataloader() and returns: DataLoader