MIONet addition and DeepONet modification.

* Create MIONet base class
* DeepONet inherits from MIONet

tests/test_model/test_mionet.py

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+import pytest
+import torch
+
+from pina import LabelTensor
+from pina.model import MIONet
+from pina.model import FeedForward
+
+data = torch.rand((20, 3))
+input_vars = ['a', 'b', 'c']
+input_ = LabelTensor(data, input_vars)
+
+
+def test_constructor():
+    branch_net1 = FeedForward(input_dimensons=1, output_dimensions=10)
+    branch_net2 = FeedForward(input_dimensons=2, output_dimensions=10)
+    trunk_net = FeedForward(input_dimensons=1, output_dimensions=10)
+    networks = {branch_net1 : ['x'],
+                branch_net2 : ['x', 'y'],
+                trunk_net : ['z']}
+    MIONet(networks=networks,
+           reduction='+',
+           aggregator='*')
+
+
+def test_constructor_fails_when_invalid_inner_layer_size():
+    branch_net1 = FeedForward(input_dimensons=1, output_dimensions=10)
+    branch_net2 = FeedForward(input_dimensons=2, output_dimensions=10)
+    trunk_net = FeedForward(input_dimensons=1, output_dimensions=12)
+    networks = {branch_net1 : ['x'],
+                branch_net2 : ['x', 'y'],
+                trunk_net : ['z']}
+    with pytest.raises(ValueError):
+        MIONet(networks=networks,
+               reduction='+',
+               aggregator='*')
+
+def test_forward_extract_str():
+    branch_net1 = FeedForward(input_dimensons=1, output_dimensions=10)
+    branch_net2 = FeedForward(input_dimensons=1, output_dimensions=10)
+    trunk_net = FeedForward(input_dimensons=1, output_dimensions=10)
+    networks = {branch_net1 : ['a'],
+                branch_net2 : ['b'],
+                trunk_net : ['c']}
+    model = MIONet(networks=networks,
+                   reduction='+',
+                   aggregator='*')
+    model(input_)
+
+def test_forward_extract_int():
+    branch_net1 = FeedForward(input_dimensons=1, output_dimensions=10)
+    branch_net2 = FeedForward(input_dimensons=1, output_dimensions=10)
+    trunk_net = FeedForward(input_dimensons=1, output_dimensions=10)
+    networks = {branch_net1 : [0],
+                branch_net2 : [1],
+                trunk_net : [2]}
+    model = MIONet(networks=networks,
+                   reduction='+',
+                   aggregator='*')
+    model(data)
+
+def test_forward_extract_str_wrong():
+    branch_net1 = FeedForward(input_dimensons=1, output_dimensions=10)
+    branch_net2 = FeedForward(input_dimensons=1, output_dimensions=10)
+    trunk_net = FeedForward(input_dimensons=1, output_dimensions=10)
+    networks = {branch_net1 : ['a'],
+                branch_net2 : ['b'],
+                trunk_net : ['c']}
+    model = MIONet(networks=networks,
+                   reduction='+',
+                   aggregator='*')
+    with pytest.raises(RuntimeError):
+        model(data)