'Save the model to a single HDF5 file.
The savefile includes:
- The model architecture, allowing to re-instantiate the model.
- The model weights.
- The state of the optimizer, allowing to resume training
exactly where you left off.
This allows you to save the entirety of the state of a model
in a single file.
Saved models can be reinstantiated via `keras.models.load_model`.
The model returned by `load_model`
is a compiled model ready to be used (unless the saved model
was never compiled in the first place).
# Arguments
filepath: String, path to the file to save the weights to.
overwrite: Whether to silently overwrite any existing file at the
target location, or provide the user with a manual prompt.
include_optimizer: If True, save optimizer\'s state together.
# Example
```python
from keras.models import load_model
model.save(\'my_model.h5\')  # creates a HDF5 file \'my_model.h5\'
del model  # deletes the existing model
# returns a compiled model
# identical to the previous one
model = load_model(\'my_model.h5\')'

def save(self, filepath, overwrite=True, include_optimizer=True):

    from ..models import save_model
    save_model(self, filepath, overwrite, include_optimizer)
