change note about deprecated ScalarizedObjective in favor of ScalarizedPosteriorTransform (#1898)

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## Motivation

The example usage at the end was already updated to use `ScalarizedPosteriorTransform`.

facebook/Ax#1312

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

Yes

Pull Request resolved: #1898

Test Plan:
N/A

## Related PRs

Doc change

Reviewed By: esantorella

Differential Revision: D46882718

Pulled By: saitcakmak

fbshipit-source-id: 152df53b3185f39734f545b3ed32ffde1e99c5cd

Author: sgbaird

tutorials/custom_acquisition.ipynb

@@ -304,7 +304,7 @@
     "\n",
     "We can also write an *analytic* version of UCB for a multi-output model, assuming a multivariate normal posterior and `q=1`. The new class `ScalarizedUpperConfidenceBound` subclasses `AnalyticAcquisitionFunction` instead of `MCAcquisitionFunction`. In contrast to the MC version, instead of using the weights on the MC samples, we directly scalarize the mean vector $\\mu$ and covariance matrix $\\Sigma$ and apply standard UCB on the univariate normal distribution, which has mean $w^T \\mu$ and variance $w^T \\Sigma w$. In addition to the `@t_batch_transform` decorator, here we are also using `expected_q=1` to ensure the input `X` has a `q=1`.\n",
     "\n",
-    "*Note:* BoTorch also provides a `ScalarizedObjective` abstraction that can be used with any existing analytic acqusition functions and automatically performs the scalarization we implement manually below. See the end of this tutorial for a usage example."
+    "*Note:* BoTorch also provides a `ScalarizedPosteriorTransform` abstraction that can be used with any existing analytic acqusition functions and automatically performs the scalarization we implement manually below. See the end of this tutorial for a usage example."
    ]
   },
   {