Document how to implement custom models (#2474)

Summary:
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## Motivation

Issue #2306

### Have you read the [Contributing Guidelines on pull requests](https://github.com/pytorch/botorch/blob/main/CONTRIBUTING.md#pull-requests)?

Yes. Added a tutorial which can be used for smoke tests.

Pull Request resolved: #2474

Test Plan:
Probabilistic linear regression, bayesian linear regression, and ensemble linear regression all yield optimization results close to (0, 0) which is groundtruth answer.

Random Forest doesn't seem to achieve groundtruth answer, likely due to its inability to incorporate gradient information into the optimization of the acquisition function.

## Related PRs

N/A

Reviewed By: esantorella

Differential Revision: D61612799

Pulled By: jakee417

fbshipit-source-id: 63d26c048dc4544cae37e89767e14caf732e7749

website/tutorials.json

@@ -57,6 +57,10 @@
     {
       "id": "ibnn_bo",
       "title": "Bayesian optimization with infinite-width neural networks"
+    },
+    {
+      "id": "custom_model",
+      "title": "Writing a custom model with the Model and Posterior Interfaces"
     }
   ],
   "Multi-Objective Bayesian Optimization": [