Neural Tangent Kernel integration + typo fix (#505)

* NTK weighting + typo fixing
* black code formatter + .rst docs

---------

Co-authored-by: Dario Coscia <dariocos99@gmail.com>

tests/test_weighting/test_ntk_weighting.py

@@ -0,0 +1,65 @@
+import pytest
+from pina import Trainer
+from pina.solver import PINN
+from pina.model import FeedForward
+from pina.problem.zoo import Poisson2DSquareProblem
+from pina.loss import NeuralTangentKernelWeighting
+
+problem = Poisson2DSquareProblem()
+condition_names = problem.conditions.keys()
+
+
+@pytest.mark.parametrize(
+    "model,alpha",
+    [
+        (
+            FeedForward(
+                len(problem.input_variables), len(problem.output_variables)
+            ),
+            0.5,
+        )
+    ],
+)
+def test_constructor(model, alpha):
+    NeuralTangentKernelWeighting(model=model, alpha=alpha)
+
+
+@pytest.mark.parametrize("model", [0.5])
+def test_wrong_constructor1(model):
+    with pytest.raises(ValueError):
+        NeuralTangentKernelWeighting(model)
+
+
+@pytest.mark.parametrize(
+    "model,alpha",
+    [
+        (
+            FeedForward(
+                len(problem.input_variables), len(problem.output_variables)
+            ),
+            1.2,
+        )
+    ],
+)
+def test_wrong_constructor2(model, alpha):
+    with pytest.raises(ValueError):
+        NeuralTangentKernelWeighting(model, alpha)
+
+
+@pytest.mark.parametrize(
+    "model,alpha",
+    [
+        (
+            FeedForward(
+                len(problem.input_variables), len(problem.output_variables)
+            ),
+            0.5,
+        )
+    ],
+)
+def test_train_aggregation(model, alpha):
+    weighting = NeuralTangentKernelWeighting(model=model, alpha=alpha)
+    problem.discretise_domain(50)
+    solver = PINN(problem=problem, model=model, weighting=weighting)
+    trainer = Trainer(solver=solver, max_epochs=5, accelerator="cpu")
+    trainer.train()