fnn update, pinn torch models, tests update. (#88)

* fnn update, remove labeltensors
* allow custom torch models
* updating tests

---------

Co-authored-by: Dario Coscia <dariocoscia@Dario-Coscia.local>
Co-authored-by: Dario Coscia <dariocoscia@dhcp-031.eduroam.sissa.it>

tests/test_model/test_deeponet.py

@@ -0,0 +1,31 @@
+import pytest
+import torch
+
+from pina import LabelTensor
+from pina.model import DeepONet
+from pina.model import FeedForward as FFN
+
+data = torch.rand((20, 3))
+input_vars = ['a', 'b', 'c']
+output_vars = ['d']
+input_ = LabelTensor(data, input_vars)
+
+# TODO
+
+# def test_constructor():
+#     branch = FFN(input_variables=['a', 'c'], output_variables=20)
+#     trunk = FFN(input_variables=['b'], output_variables=20)
+#     onet = DeepONet(nets=[trunk, branch], output_variables=output_vars)
+
+# def test_constructor_fails_when_invalid_inner_layer_size():
+#     branch = FFN(input_variables=['a', 'c'], output_variables=20)
+#     trunk = FFN(input_variables=['b'], output_variables=19)
+#     with pytest.raises(ValueError):
+#         DeepONet(nets=[trunk, branch], output_variables=output_vars)
+
+# def test_forward():
+#     branch = FFN(input_variables=['a', 'c'], output_variables=10)
+#     trunk = FFN(input_variables=['b'], output_variables=10)
+#     onet = DeepONet(nets=[trunk, branch], output_variables=output_vars)
+#     output_ = onet(input_)
+#     assert output_.labels == output_vars