Trainer's fit method fails when the only logger is the custom logger from the official documentation
🐛 Bug

Trainer's fit method fails when the only logger is the custom logger from the official documentation
To Reproduce


Define a custom logger as per https://pytorch-lightning.readthedocs.io/en/latest/logging.html#make-a-custom-logger
logger = MyLogger()
Define a lightning module as per https://pytorch-lightning.readthedocs.io/en/latest/new-project.html#step-1-define-lightningmodule
Load MNIST and call training as per https://pytorch-lightning.readthedocs.io/en/latest/new-project.html#step-2-fit-with-lightning-trainer with ONE MODIFICATION -  specify Trainer(gpus=1, logger = logger)

Expected behavior
Successful execution
Environment

CUDA:
- GPU:
- GeForce RTX 2070
- available:         True
- version:           10.2
Packages:
- numpy:             1.19.2
- pyTorch_debug:     False
- pyTorch_version:   1.6.0
- pytorch-lightning: 1.0.3
- tqdm:              4.50.2
System:
- OS:                Linux
- architecture:
- 64bit
- ELF
- processor:
- python:            3.8.5
- version:           #1 SMP PREEMPT Sat Oct 17 11:06:19 UTC 2020

Additional context
Stack trace:

`
PU available: True, used: True
TPU available: False, using: 0 TPU cores
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]

TypeError                                 Traceback (most recent call last)
 in 
5 # trainer = pl.Trainer(gpus=8) (if you have GPUs)
6 trainer = pl.Trainer(gpus=1, logger = logger)
----> 7 trainer.fit(autoencoder, train_loader)
~/.conda/envs/dlschool0/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py in fit(self, model, train_dataloader, val_dataloaders, datamodule)
437         self.call_hook('on_fit_start')
438
--> 439         results = self.accelerator_backend.train()
440         self.accelerator_backend.teardown()
441
~/.conda/envs/dlschool0/lib/python3.8/site-packages/pytorch_lightning/accelerators/gpu_accelerator.py in train(self)
49
50         # set up training routine
---> 51         self.trainer.train_loop.setup_training(model)
52
53         # train or test
~/.conda/envs/dlschool0/lib/python3.8/site-packages/pytorch_lightning/trainer/training_loop.py in setup_training(self, model)
144         # --------------------------
145         # on pretrain routine start
--> 146         self.trainer.on_pretrain_routine_start(ref_model)
147         if self.trainer.is_function_implemented('on_pretrain_routine_start'):
148             ref_model.on_pretrain_routine_start()
~/.conda/envs/dlschool0/lib/python3.8/site-packages/pytorch_lightning/trainer/callback_hook.py in on_pretrain_routine_start(self, model)
120         """Called when the train begins."""
121         for callback in self.callbacks:
--> 122             callback.on_pretrain_routine_start(self, model)
123
124     def on_pretrain_routine_end(self, model):
~/.conda/envs/dlschool0/lib/python3.8/site-packages/pytorch_lightning/callbacks/model_checkpoint.py in on_pretrain_routine_start(self, trainer, pl_module)
159         When pretrain routine starts we build the ckpt dir on the fly
160         """
--> 161         self.__resolve_ckpt_dir(trainer, pl_module)
162
163     def on_validation_end(self, trainer, pl_module):
~/.conda/envs/dlschool0/lib/python3.8/site-packages/pytorch_lightning/callbacks/model_checkpoint.py in __resolve_ckpt_dir(self, trainer, pl_module)
405             version, name = trainer.accelerator_backend.broadcast((version, trainer.logger.name))
406
--> 407             ckpt_path = os.path.join(
408                 save_dir, name, version, "checkpoints"
409             )
~/.conda/envs/dlschool0/lib/python3.8/posixpath.py in join(a, *p)
88                 path += sep + b
89     except (TypeError, AttributeError, BytesWarning):
---> 90         genericpath._check_arg_types('join', a, *p)
91         raise
92     return path
~/.conda/envs/dlschool0/lib/python3.8/genericpath.py in _check_arg_types(funcname, *args)
150             hasbytes = True
151         else:
--> 152             raise TypeError(f'{funcname}() argument must be str, bytes, or '
153                             f'os.PathLike object, not {s.class.name!r}') from None
154     if hasstr and hasbytes:
TypeError: join() argument must be str, bytes, or os.PathLike object, not 'method'
`