Fixing 404 errors of links to notebooks in the documentation (#143)

I assume that the notebooks have been moved, but the documentation links
did not reflect that

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docs/notebooks.rst

@@ -14,7 +14,7 @@ The first set of notebooks demonstrates the basic mechanics of OMLT and shows ho
 
 * `index_handling.ipynb <https://github.com/cog-imperial/OMLT/blob/main/docs/notebooks/neuralnet/index_handling.ipynb>`_ shows how to use `IndexMapper` to handle the mappings between indexes.
 
-* `bo_with_trees.ipynb <https://github.com/cog-imperial/OMLT/blob/main/docs/notebooks/bo_with_trees.ipynb>`_ incorporates gradient-boosted trees into a Bayesian optimization loop to optimize the Rosenbrock function.
+* `bo_with_trees.ipynb <https://github.com/cog-imperial/OMLT/blob/main/docs/notebooks/trees/bo_with_trees.ipynb>`_ incorporates gradient-boosted trees into a Bayesian optimization loop to optimize the Rosenbrock function.
 
 * `linear_tree_formulations.ipynb <https://github.com/cog-imperial/OMLT/blob/main/docs/notebooks/trees/linear_tree_formulations.ipynb>`_ showcases the different linear model decision tree formulations available in OMLT.
 
@@ -24,7 +24,7 @@ The second set of notebooks gives application-specific examples:
 
 * `mnist_example_convolutional.ipynb <https://github.com/cog-imperial/OMLT/blob/main/docs/notebooks/neuralnet/mnist_example_convolutional.ipynb>`_ trains a convolutional neural network on MNIST and uses OMLT to find adversarial examples.
 
-* `graph_neural_network_formulation.ipynb <https://github.com/cog-imperial/OMLT/blob/main/docs/notebooks/graph_neural_network_formulation.ipynb>`_ transforms graph neural networks into OMLT and builds formulation to solve optimization problems.
+* `graph_neural_network_formulation.ipynb <https://github.com/cog-imperial/OMLT/blob/main/docs/notebooks/neuralnet/graph_neural_network_formulation.ipynb>`_ transforms graph neural networks into OMLT and builds formulation to solve optimization problems.
 
 * `auto-thermal-reformer.ipynb <https://github.com/cog-imperial/OMLT/blob/main/docs/notebooks/neuralnet/auto-thermal-reformer.ipynb>`_ develops a neural network surrogate (using sigmoid activations) with data from a process model built using `IDAES-PSE <https://github.com/IDAES/idaes-pse>`_.