Tensorboard logger fails to save model OmegaConf hparams 
🐛 Bug
The Tensorboard logger fails to log module hyperparameters configured with OmegaConf. This happens when updating the logger hparams here:

The trainer calls the logger's log_hyperparams here:
Inside log_hyperparams the logger's hparams are updated here. This causes the hparams type to now be dict instead of DictConfig
As a result, this branch in [save_hparams_to_yaml](https://github.com/PyTorchLightning/pytorch-lightning/blob/master/pytorch_lightning/core/saving.py#L330-L333) is never triggered

This is the stacktrace when logging hyperparams: https://gist.github.com/ananthsub/7acfdb0e0f551ed030f05f7674c37b46
To Reproduce
Code sample
A hacky fix would be something like changing the hparams update to use this inside the tensorboard logger:
if isinstance(params, Container):
   self.hparams = OmegaConf.merge(self.hparams, params)
else:
    self.hparams.update(params)


Expected behavior
Environment
Please copy and paste the output from our
environment collection script
(or fill out the checklist below manually).
You can get the script and run it with:
wget https://raw.githubusercontent.com/PyTorchLightning/pytorch-lightning/master/tests/collect_env_details.py
# For security purposes, please check the contents of collect_env_details.py before running it.
python collect_env_details.py


PyTorch Version (e.g., 1.0):
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Build command you used (if compiling from source):
Python version:
CUDA/cuDNN version:
GPU models and configuration:
Any other relevant information:

Additional context