Informer not working on basic example
### System Info

I am trying minimal example code of Informer specified on HuggingFace website: https://huggingface.co/docs/transformers/model_doc/informer 
however I am getting this error while running that:
```
  File "C:\Users\User\anaconda3\lib\site-packages\torch\nn\modules\linear.py", line 114, in forward
    return F.linear(input, self.weight, self.bias)

RuntimeError: mat1 and mat2 shapes cannot be multiplied (1536x23 and 22x32)
```
I am using 4.27.4 version of transformers library.

The code from website used: 
```
from huggingface_hub import hf_hub_download
import torch
from transformers import InformerModel

file = hf_hub_download(
    repo_id="kashif/tourism-monthly-batch", filename="train-batch.pt", repo_type="dataset"
)
batch = torch.load(file)

model = InformerModel.from_pretrained("huggingface/informer-tourism-monthly")

# during training, one provides both past and future values
# as well as possible additional features
outputs = model(
    past_values=batch["past_values"],
    past_time_features=batch["past_time_features"],
    past_observed_mask=batch["past_observed_mask"],
    static_categorical_features=batch["static_categorical_features"],
    static_real_features=batch["static_real_features"],
    future_values=batch["future_values"],
    future_time_features=batch["future_time_features"],
)

last_hidden_state = outputs.last_hidden_state
```

The full traceback: 
```
outputs = model(

  File "C:\Users\User\anaconda3\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)

  File "C:\Users\User\anaconda3\lib\site-packages\transformers\models\informer\modeling_informer.py", line 1870, in forward
    outputs = self.model(

  File "C:\Users\User\anaconda3\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)

  File "C:\Users\User\anaconda3\lib\site-packages\transformers\models\informer\modeling_informer.py", line 1704, in forward
    encoder_outputs = self.encoder(

  File "C:\Users\User\anaconda3\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)

  File "C:\Users\User\anaconda3\lib\site-packages\transformers\models\informer\modeling_informer.py", line 1178, in forward
    hidden_states = self.value_embedding(inputs_embeds)

  File "C:\Users\User\anaconda3\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)

  File "C:\Users\User\anaconda3\lib\site-packages\transformers\models\informer\modeling_informer.py", line 305, in forward
    return self.value_projection(x)

  File "C:\Users\User\anaconda3\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)

  File "C:\Users\User\anaconda3\lib\site-packages\torch\nn\modules\linear.py", line 114, in forward
    return F.linear(input, self.weight, self.bias)

RuntimeError: mat1 and mat2 shapes cannot be multiplied (1536x23 and 22x32)
```



### Who can help?

_No response_

### Information

- [x] The official example scripts
- [ ] My own modified scripts

### Tasks

- [x] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)

### Reproduction

```
from huggingface_hub import hf_hub_download
import torch
from transformers import InformerModel

file = hf_hub_download(
    repo_id="kashif/tourism-monthly-batch", filename="train-batch.pt", repo_type="dataset"
)
batch = torch.load(file)

model = InformerModel.from_pretrained("huggingface/informer-tourism-monthly")

# during training, one provides both past and future values
# as well as possible additional features
outputs = model(
    past_values=batch["past_values"],
    past_time_features=batch["past_time_features"],
    past_observed_mask=batch["past_observed_mask"],
    static_categorical_features=batch["static_categorical_features"],
    static_real_features=batch["static_real_features"],
    future_values=batch["future_values"],
    future_time_features=batch["future_time_features"],
)

last_hidden_state = outputs.last_hidden_state
```

### Expected behavior

no error, `outputs` contains model output
cc @kashif <|||||>thanks @sgugger having a look!<|||||>@SlimakSlimak just to test: the model works if you comment out the `static_real_features` argument to the `model`?
<|||||>Hi, yes that works, thank you!<|||||>This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

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