question: After updating to 1.5.2, NotImplementedError: `train_dataloader`
context: When I update the following repository to 1.5.2, I get the following error. Is there an implementation guide somewhere?
  Keiku/PyTorch-Lightning-CIFAR10: "Not too complicated" training code for CIFAR-10 by PyTorch Lightning https://github.com/Keiku/PyTorch-Lightning-CIFAR10
  (PyTorch-Lightning-CIFAR10) ⋊> /m/n/k/c/PyTorch-Lightning-CIFAR10 on main ⨯
  pipenv run python train.py +experiments=train_exp01 hydra.run.dir=outputs/train_exp01
  
  Global seed set to 0
  GPU available: True, used: False
  TPU available: False, using: 0 TPU cores
  IPU available: False, using: 0 IPUs
  /home/anasys/.local/share/virtualenvs/PyTorch-Lightning-CIFAR10-fAnnMMRx/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py:1579: UserWarning: GPU available but not used. Set the gpus flag in your trainer `Trainer(gpus=1)` or script `--gpus=1`.
    rank_zero_warn(
  Traceback (most recent call last):
    File "train.py", line 68, in main
      trainer.fit(model,
    File "/home/anasys/.local/share/virtualenvs/PyTorch-Lightning-CIFAR10-fAnnMMRx/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 737, in fit
      self._call_and_handle_interrupt(
    File "/home/anasys/.local/share/virtualenvs/PyTorch-Lightning-CIFAR10-fAnnMMRx/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 682, in _call_and_handle_interrupt
      return trainer_fn(*args, **kwargs)
    File "/home/anasys/.local/share/virtualenvs/PyTorch-Lightning-CIFAR10-fAnnMMRx/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 772, in _fit_impl
      self._run(model, ckpt_path=ckpt_path)
    File "/home/anasys/.local/share/virtualenvs/PyTorch-Lightning-CIFAR10-fAnnMMRx/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1141, in _run
      self.accelerator.setup(self)
    File "/home/anasys/.local/share/virtualenvs/PyTorch-Lightning-CIFAR10-fAnnMMRx/lib/python3.8/site-packages/pytorch_lightning/accelerators/cpu.py", line 35, in setup
      return super().setup(trainer)
    File "/home/anasys/.local/share/virtualenvs/PyTorch-Lightning-CIFAR10-fAnnMMRx/lib/python3.8/site-packages/pytorch_lightning/accelerators/accelerator.py", line 93, in setup
      self.setup_optimizers(trainer)
    File "/home/anasys/.local/share/virtualenvs/PyTorch-Lightning-CIFAR10-fAnnMMRx/lib/python3.8/site-packages/pytorch_lightning/accelerators/accelerator.py", line 351, in setup_optimizers
      optimizers, lr_schedulers, optimizer_frequencies = self.training_type_plugin.init_optimizers(
    File "/home/anasys/.local/share/virtualenvs/PyTorch-Lightning-CIFAR10-fAnnMMRx/lib/python3.8/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py", line 245, in init_optimizers
      return trainer.init_optimizers(model)
    File "/home/anasys/.local/share/virtualenvs/PyTorch-Lightning-CIFAR10-fAnnMMRx/lib/python3.8/site-packages/pytorch_lightning/trainer/optimizers.py", line 35, in init_optimizers
      optim_conf = self.call_hook("configure_optimizers", pl_module=pl_module)
    File "/home/anasys/.local/share/virtualenvs/PyTorch-Lightning-CIFAR10-fAnnMMRx/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1496, in call_hook
      output = model_fx(*args, **kwargs)
    File "/mnt/nfs/kuroyanagi/clones/PyTorch-Lightning-CIFAR10/model.py", line 73, in configure_optimizers
      total_steps = cfg.train.num_epochs * len(self.train_dataloader())
    File "/home/anasys/.local/share/virtualenvs/PyTorch-Lightning-CIFAR10-fAnnMMRx/lib/python3.8/site-packages/pytorch_lightning/core/hooks.py", line 477, in train_dataloader
      raise NotImplementedError("`train_dataloader` must be implemented to be used with the Lightning Trainer")
  NotImplementedError: `train_dataloader` must be implemented to be used with the Lightning Trainer
  
  Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.
  (PyTorch-Lightning-CIFAR10) ⋊> /m/n/k/c/PyTorch-Lightning-CIFAR10 on main ⨯
answer: for this problem, tracking issue: #10430