Allow for observation noise without mask in ModelListGP (#2735)

Summary:
<!--
Thank you for sending the PR! We appreciate you spending the time to make BoTorch better.

Help us understand your motivation by explaining why you decided to make this change.

You can learn more about contributing to BoTorch here: https://github.com/pytorch/botorch/blob/main/CONTRIBUTING.md
-->

## Motivation

Fixes #2734

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

Pull Request resolved: #2735

Test Plan:
I've added a test to the unit test, in addition, a separate example:

```python
import torch
from botorch.fit import fit_gpytorch_mll
from botorch.models.gp_regression import SingleTaskGP
from botorch.models.model_list_gp_regression import ModelListGP
from botorch.sampling.list_sampler import ListSampler
from botorch.sampling.normal import SobolQMCNormalSampler
from gpytorch.mlls.sum_marginal_log_likelihood import SumMarginalLogLikelihood

model = ModelListGP(
    *[SingleTaskGP(train_X=torch.tensor([[0.0], [1.0]]), train_Y=torch.tensor([[0.0], [1.0]])) for _ in range(2)]
)
mll = SumMarginalLogLikelihood(model.likelihood, model)
mll = fit_gpytorch_mll(mll)
sampler = ListSampler(*[SobolQMCNormalSampler(sample_shape=torch.Size([1])) for _ in range(2)])

# Works:
model.fantasize(
    torch.tensor([[0.5]]),
    sampler=sampler,
    observation_noise=torch.tensor([[0.2, 0.2]]),
    evaluation_mask=torch.tensor([[1.0, 1.0]], dtype=torch.bool),
)

# Now also works:
model.fantasize(
    torch.tensor([[0.5]]),
    sampler=sampler,
    observation_noise=torch.tensor([[0.2, 0.2]]),
)
```

Reviewed By: esantorella

Differential Revision: D69261331

Pulled By: saitcakmak

fbshipit-source-id: f51ab84144ecfcf1b142ad7b447fc6c56d518202

botorch/models/model.py

@@ -661,6 +661,8 @@ def fantasize(
                 sampler_i = (
                     sampler.samplers[i] if isinstance(sampler, ListSampler) else sampler
                 )
+                if observation_noise is not None:
+                    observation_noise_i = observation_noise[..., i : i + 1]
 
             fant_model = self.models[i].fantasize(
                 X=X_i,

test/models/test_model_list_gp_regression.py

@@ -479,6 +479,27 @@ def test_fantasize(self):
                     (3, 2), 0.3, dtype=x1.dtype, device=x1.device
                 )
                 observation_noise[:, 1] = 0.4
+
+                # check observation noise without mask
+                fm = modellist.fantasize(
+                    torch.rand(3, 2),
+                    sampler=ListSampler(sampler1, sampler2),
+                    observation_noise=observation_noise,
+                )
+                for i in range(2):
+                    fm_i = fm.models[i]
+                    self.assertIsInstance(fm_i, SingleTaskGP)
+                    self.assertIsInstance(fm_i.likelihood, FixedNoiseGaussianLikelihood)
+                    self.assertEqual(fm_i.train_inputs[0].shape, torch.Size([2, 8, 2]))
+                    self.assertEqual(fm_i.train_targets.shape, torch.Size([2, 8]))
+                    # check observation_noise
+                    self.assertTrue(
+                        torch.equal(
+                            fm_i.likelihood.noise[..., -3:], observation_noise[:, i]
+                        )
+                    )
+
+                # check masked noise
                 for obs_noise in (None, observation_noise):
                     fm = modellist.fantasize(
                         torch.rand(3, 2),