code: def __init__(self, size, length):
      self.len = length
      self.float_data = torch.randn(length, size)
      self.int_data = torch.randint(10, (length, 1))
  
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 1 to create a new RandomFloatIntDataset object from inherited base classes: Dataset with example: obj = RandomFloatIntDataset(size, length)