Add Averaging Neural Operator with tests and a tutorial (#230)

* add Averaging Neural Operator with tests
* add backward test
* minor changes
* doc addition

---------

Co-authored-by: Dario Coscia <dariocoscia@Dario-Coscia.local>

tests/test_model/test_avno.py

@@ -0,0 +1,62 @@
+import torch
+from pina.model import AveragingNeuralOperator
+from pina import LabelTensor
+
+output_numb_fields = 5
+batch_size = 15
+
+
+def test_constructor():
+    input_numb_fields = 1
+    output_numb_fields = 1
+    #minimuum constructor
+    AveragingNeuralOperator(input_numb_fields,
+         output_numb_fields,
+         coordinates_indices=['p'],
+         field_indices=['v'])
+
+    #all constructor
+    AveragingNeuralOperator(input_numb_fields,
+         output_numb_fields,
+         inner_size=5,
+         n_layers=5,
+         func=torch.nn.ReLU,
+         coordinates_indices=['p'],
+         field_indices=['v'])
+
+
+def test_forward():
+    input_numb_fields = 1
+    output_numb_fields = 1
+    dimension = 1
+    input_ = LabelTensor(
+        torch.rand(batch_size, 1000, input_numb_fields + dimension), ['p', 'v'])
+    ano = AveragingNeuralOperator(input_numb_fields,
+               output_numb_fields,
+               dimension=dimension,
+               coordinates_indices=['p'],
+               field_indices=['v'])
+    out = ano(input_)
+    assert out.shape == torch.Size(
+        [batch_size, input_.shape[1], output_numb_fields])
+
+
+def test_backward():
+    input_numb_fields = 1
+    dimension = 1
+    output_numb_fields = 1
+    input_ = LabelTensor(
+        torch.rand(batch_size, 1000, dimension + input_numb_fields), 
+        ['p', 'v'])
+    input_ = input_.requires_grad_()
+    avno = AveragingNeuralOperator(input_numb_fields,
+                output_numb_fields,
+                dimension=dimension,
+                coordinates_indices=['p'],
+                field_indices=['v'])
+    out = avno(input_)
+    tmp = torch.linalg.norm(out)
+    tmp.backward()
+    grad = input_.grad
+    assert grad.shape == torch.Size(
+        [batch_size, input_.shape[1], dimension + input_numb_fields])