Fix AutoformerForPrediction example code (#29639)

Removed static_real_features from AutoformerForPrediction example code
Signed-off-by: Maciej Torhan <maciek97x@gmail.com>

src/transformers/models/autoformer/modeling_autoformer.py

@@ -1853,7 +1853,6 @@ class AutoformerForPrediction(AutoformerPreTrainedModel):
         ...     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"],
         ... )
@@ -1869,12 +1868,54 @@ class AutoformerForPrediction(AutoformerPreTrainedModel):
         ...     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_time_features=batch["future_time_features"],
         ... )
 
         >>> mean_prediction = outputs.sequences.mean(dim=1)
-        ```"""
+        ```
+
+        <Tip>
+
+        The AutoformerForPrediction can also use static_real_features. To do so, set num_static_real_features in
+        AutoformerConfig based on number of such features in the dataset (in case of tourism_monthly dataset it
+        is equal to 1), initialize the model and call as shown below:
+
+        ```
+        >>> from huggingface_hub import hf_hub_download
+        >>> import torch
+        >>> from transformers import AutoformerConfig, AutoformerForPrediction
+
+        >>> file = hf_hub_download(
+        ...     repo_id="hf-internal-testing/tourism-monthly-batch", filename="train-batch.pt", repo_type="dataset"
+        ... )
+        >>> batch = torch.load(file)
+
+        >>> # check number of static real features
+        >>> num_static_real_features = batch["static_real_features"].shape[-1]
+
+        >>> # load configuration of pretrained model and override num_static_real_features
+        >>> configuration = AutoformerConfig.from_pretrained(
+        ...     "huggingface/autoformer-tourism-monthly",
+        ...     num_static_real_features=num_static_real_features,
+        ... )
+        >>> # we also need to update feature_size as it is not recalculated
+        >>> configuration.feature_size += num_static_real_features
+
+        >>> model = AutoformerForPrediction(configuration)
+
+        >>> 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"],
+        ... )
+        ```
+
+        </Tip>
+        """
 
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
         if future_values is not None: