Make the Python Wrapper more Hackable and simplify Quantization (#1010)

* Some first `Module` implementations

* Add `state_dict` and `load_state_dict` functionality

* Move modules around and create `candle.nn.Linear`

* Add `nn.Embedding` and `nn.LayerNorm`

* Add BERT implementation

* Batch q-matmul

* Automatically dequantize `QTensors` if a `Tensor` is expected

* Add Module `.to()`, `.cuda()`, `cpu()` and `.type()` functionality

* Unittests for `Module`, `Tensor` and `candle.utils`

* Add `pytorch` like slicing to `Tensor`

* Cleanup and BERT fixes

* `black` formatting + unit-test for `nn.Linear`

* Refactor slicing implementation

candle-pyo3/test.py

@@ -7,7 +7,7 @@ print(t + t)
 
 t = candle.Tensor([3.0, 1, 4, 1, 5, 9, 2, 6])
 print(t)
-print(t+t)
+print(t + t)
 
 t = t.reshape([2, 4])
 print(t.matmul(t.t()))