code: def get_dataloader(self):
      # multiply * 8 just in case world size is larger than 1
      dataset_size = self.num_steps * self.batch_size * 8
      inputs = torch.rand(dataset_size, 3, 32, 32)
      labels = torch.randint(0, 10, (dataset_size,))
      dataset = TensorDataset(inputs, labels)
      return DataLoader(
          dataset,
          batch_size=self.batch_size,
          num_workers=2,
      )
  
file_path: pytorch-lightning/tests/parity_fabric/models.py
imports: from abc import ABC, abstractmethod
  from typing import Callable
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.optim import Optimizer
  from torch.utils.data import DataLoader, TensorDataset
label_desc: Use image node_id 4 for calling the ConvNet obj's underlying member method code with example usage: obj.get_dataloader() and returns: DataLoader