[Bug] Encoder in diffusers.models.autoencoders.vae's forward method return type mismatch leads to AttributeError

### Describe the bug

**Issue Description:**
When using the Encoder from the` diffusers.models.autoencoders.vae module`, calling its forward method returns a value type mismatch, resulting in an AttributeError during subsequent processing. Specifically, when calling the Encoder's forward method, the returned result is a tuple, while the subsequent code expects to receive a tensor.

### Reproduction

Please use the following code to reproduce the issue
```python
from diffusers.models.autoencoders.vae import Encoder
import torch

encoder = Encoder(
down_block_types=["DownBlock2D", "DownBlock2D"],
block_out_channels=[64, 64],
)

encoder(torch.randn(1, 3, 256, 256)).shape
```

**Expected Behavior:**
The Encoder's forward method in `diffusers.models.autoencoders.vae` should return a tensor for further processing.

**Actual Behavior:**
Running the above code results in the following error:
```txt
AttributeError: 'tuple' object has no attribute 'dim'
```

**Additional Information:**
- Error log:
```txt
Traceback (most recent call last):
File "main.py", line 9, in
encoder(torch.randn(1, 3, 256, 256)).shape
...
File "python3.11/site-packages/diffusers/models/autoencoders/vae.py", line 172, in forward
sample = down_block(sample)
...
File "python3.11/site-packages/diffusers/models/autoencoders/vae.py", line 172, in forward
hidden_states = resnet(hidden_states, temb)
...
File "python3.11/site-packages/diffusers/models/autoencoders/vae.py", line 172, in forward
hidden_states = self.norm1(hidden_states)
File "python3.11/site-packages/torch/nn/modules/normalization.py", line 313, in forward
return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps)
File "python3.11/site-packages/torch/nn/functional.py", line 2947, in group_norm
if input.dim() < 2:
AttributeError: 'tuple' object has no attribute 'dim'
```
- **Relevant code snippet:**
- In` diffusers/models/autoencoders/vae.py`, lines 171-173:
```python
for down_block in self.down_blocks:
sample = down_block(sample)
```

- `DownBlock2D`'s `forward `method declaration:
```python
def forward(
self, hidden_states: torch.Tensor, temb: Optional[torch.Tensor] = None, *args, **kwargs
) -> Tuple[torch.Tensor, Tuple[torch.Tensor, ...]]:
```

### Logs

_No response_

### System Info

- 🤗 Diffusers version: 0.31.0
- Platform: Linux-5.15.167.4-microsoft-standard-WSL2-x86_64-with-glibc2.39
- Running on Google Colab?: No
- Python version: 3.11.11
- PyTorch version (GPU?): 2.5.1 (True)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Huggingface_hub version: 0.26.5
- Transformers version: 4.47.0
- Accelerate version: 1.2.1
- PEFT version: 0.14.0
- Bitsandbytes version: not installed
- Safetensors version: 0.4.5
- xFormers version: not installed
- Accelerator: NVIDIA GeForce RTX 3090, 24576 MiB
- Using GPU in script?: Yes
- Using distributed or parallel set-up in script?: No

### Who can help?

@DN6 @sayakpaul

Start by running the supplied reproduction against diffusers/models/autoencoders/vae.py, then inspect Encoder.forward and the DownBlock2D.forward return declaration around the referenced loop. Confirm the expected output contract and verify that calling Encoder(...).shape no longer raises the reported AttributeError.

Scritto dal modello di indicizzazione a partire dal testo della issue.