code: def dummy_dataloaders(
      a=2,
      b=3,
      batch_size=16,
      n_train_batches: int = 10,
      n_valid_batches: int = 2,
  ):
      "Generates a tuple of dummy DataLoaders to test with"
  
      def get_dataset(n_batches):
          x = torch.randn(batch_size * n_batches, 1)
          return TensorDataset(
              x,
              a * x + b + 0.1 * torch.randn(batch_size * n_batches, 1),
          )
  
      train_dataset = get_dataset(n_train_batches)
      valid_dataset = get_dataset(n_valid_batches)
      train_dataloader = DataLoader(
          train_dataset,
          shuffle=True,
          batch_size=batch_size,
          num_workers=4,
      )
      valid_dataloader = DataLoader(
          valid_dataset,
          shuffle=False,
          batch_size=batch_size,
          num_workers=4,
      )
      return (train_dataloader, valid_dataloader)
  
imports: import inspect
  import logging
  import os
  import random
  import shutil
  import tempfile
  import unittest
  import uuid
  from contextlib import contextmanager
  import pytest
  import torch
  from parameterized import parameterized_class
  from torch import nn
  from torch.utils.data import DataLoader, TensorDataset
  from accelerate import Accelerator
  from accelerate.test_utils import device_count, execute_subprocess_async, require_non_cpu
  from accelerate.utils import ProjectConfiguration, set_seed
label_desc: Use image node_id 1 for calling a global function with example usage: dummy_dataloaders(a, b, batch_size, n_train_batches, n_valid_batches) and returns: train_dataloader,  valid_dataloader, TensorDataset