PyTorch, called SimpleModel as follows:
```python
from torch import nn
class SimpleModel(nn.Module):
    def init(self):
        super().init()
        self.dense = nn.Linear(10, 10)
        self.intermediate = nn.Linear(10, 10)
        self.layer_norm = nn.LayerNorm(10)
```
Now we can create an instance of this model definition which will fill all weights: dense, intermediate,
layer_norm with random weights. We can print the model to see its architecture
```python
model = SimpleModel()
print(model)
```
This will print out the following:
SimpleModel(
  (dense): Linear(in_features=10, out_features=10, bias=True)
  (intermediate): Linear(in_features=10, out_features=10, bias=True)
  (layer_norm): LayerNorm((10,), eps=1e-05, elementwise_affine=True)
)
We can see that the layer names are defined by the name of the class attribute in PyTorch. You can print out the weight
