Lightning modules are strict superclasses of torch.nn.Module. A LightningModule offers the following in addition to that API.

---    
### freeze 
Freeze all params for inference
```{.python}
model = MyLightningModule(...)
model.freeze()
```

---    
### load_from_metrics
This is the easiest/fastest way which uses the meta_tags.csv file from test-tube to rebuild the model.
The meta_tags.csv file can be found in the test-tube experiment save_dir.       

```{.python}
pretrained_model = MyLightningModule.load_from_metrics(
    weights_path='/path/to/pytorch_checkpoint.ckpt',
    tags_csv='/path/to/test_tube/experiment/version/meta_tags.csv',
    on_gpu=True,
    map_location=None
)
    
# predict
pretrained_model.eval()
pretrained_model.freeze()
y_hat = pretrained_model(x)
```

**Params**    

| Param  | description  |
|---|---|
|  weights_path | Path to a PyTorch checkpoint  |
|  tags_csv | Path to meta_tags.csv file generated by the test-tube Experiment  |
|  on_gpu | if True, puts model on GPU. Make sure to use transforms option if model devices have changed  |
|  map_location | A dictionary mapping saved weight GPU devices to new GPU devices |

**Returns**    

LightningModule - The pretrained LightningModule

---    
### unfreeze 
Unfreeze all params for inference
```{.python}
model = MyLightningModule(...)
model.unfreeze()
```