Low Rank Neural Operator (#270)

* add the Low Rank Neural Operator as Model
* add the Low Rank Layer as Layer
* adding tests
* adding doc 

---------

Co-authored-by: Dario Coscia <dariocoscia@Dario-Coscia.local>
Co-authored-by: Nicola Demo <demo.nicola@gmail.com>

tests/test_layers/test_lnolayer.py

@@ -0,0 +1,58 @@
+import torch
+import pytest
+
+from pina.model.layers import LowRankBlock
+from pina import LabelTensor
+
+
+input_dimensions=2
+embedding_dimenion=1
+rank=4
+inner_size=20
+n_layers=2
+func=torch.nn.Tanh
+bias=True
+
+def test_constructor():
+    LowRankBlock(input_dimensions=input_dimensions,
+                 embedding_dimenion=embedding_dimenion,
+                 rank=rank,
+                 inner_size=inner_size,
+                 n_layers=n_layers,
+                 func=func,
+                 bias=bias)
+    
+def test_constructor_wrong():
+    with pytest.raises(ValueError):
+        LowRankBlock(input_dimensions=input_dimensions,
+                 embedding_dimenion=embedding_dimenion,
+                 rank=0.5,
+                 inner_size=inner_size,
+                 n_layers=n_layers,
+                 func=func,
+                 bias=bias)
+           
+def test_forward():
+    block = LowRankBlock(input_dimensions=input_dimensions,
+                 embedding_dimenion=embedding_dimenion,
+                 rank=rank,
+                 inner_size=inner_size,
+                 n_layers=n_layers,
+                 func=func,
+                 bias=bias)
+    data = LabelTensor(torch.rand(10, 30, 3), labels=['x', 'y', 'u'])
+    block(data.extract('u'), data.extract(['x', 'y']))
+
+def test_backward():
+    block = LowRankBlock(input_dimensions=input_dimensions,
+                 embedding_dimenion=embedding_dimenion,
+                 rank=rank,
+                 inner_size=inner_size,
+                 n_layers=n_layers,
+                 func=func,
+                 bias=bias)
+    data = LabelTensor(torch.rand(10, 30, 3), labels=['x', 'y', 'u'])
+    data.requires_grad_(True)
+    out = block(data.extract('u'), data.extract(['x', 'y']))
+    loss = out.mean()
+    loss.backward()