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

Summary:
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## 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

Author: swierh

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),