fix docstrings

src/transformers/models/timesfm/modeling_timesfm.py

@@ -185,7 +185,7 @@ class TimesFMPositionalEmbedding(nn.Module):
 
 
 class TimesFMAttention(nn.Module):
-    """Implements the attention used in TimesFM."""
+    """Implements the attention used in TimesFM. One key diffrence is that there is _per_dim_scaling of the query."""
 
     def __init__(self, config: TimesFMConfig):
         super().__init__()
@@ -655,7 +655,8 @@ class TimesFMDecoder(TimesFMPreTrainedModel):
         freq: torch.Tensor,
         output_attentions: bool = False,
         output_hidden_states: bool = False,
-    ) -> TimesFMDecoderOutput:
+        return_dict: bool = True,
+    ) -> TimesFMDecoderOutput | tuple[torch.Tensor, ...]:
         model_input, patched_padding, stats, _ = self._preprocess_input(
             input_ts=input_ts,
             input_padding=input_padding,
@@ -674,13 +675,22 @@ class TimesFMDecoder(TimesFMPreTrainedModel):
         else:
             all_hidden_states = None
 
-        return TimesFMDecoderOutput(
-            last_hidden_state=transformer_output.last_hidden_state,
-            hidden_states=all_hidden_states,
-            attentions=transformer_output.attentions if output_attentions else None,
-            loc=stats[0],
-            scale=stats[1],
-        )
+        if return_dict:
+            return TimesFMDecoderOutput(
+                last_hidden_state=transformer_output.last_hidden_state,
+                hidden_states=all_hidden_states,
+                attentions=transformer_output.attentions if output_attentions else None,
+                loc=stats[0],
+                scale=stats[1],
+            )
+        else:
+            return (
+                transformer_output.last_hidden_state,
+                all_hidden_states,
+                transformer_output.attentions,
+                stats[0],
+                stats[1],
+            )
 
 
 class TimesFMModelForPrediction(TimesFMPreTrainedModel):
@@ -778,7 +788,7 @@ class TimesFMModelForPrediction(TimesFMPreTrainedModel):
         return_forecast_on_context: bool = False,
         output_attentions: bool = False,
         output_hidden_states: bool = False,
-    ):
+    ) -> tuple[torch.Tensor, ...]:
         """Auto-regressive decoding without caching.
 
         Args:
@@ -799,6 +809,9 @@ class TimesFMModelForPrediction(TimesFMPreTrainedModel):
             B x H' x (1 + # quantiles).
           In particular, if return_forecast_on_context is True, H' is H plus
           the forecastable context length, i.e. context_len - (first) patch_len.
+
+        Raises:
+          ValueError: If the paddings do not match the input + horizon_len.
         """
         final_out = input_ts
         context_len = final_out.shape[1]
@@ -871,7 +884,7 @@ class TimesFMModelForPrediction(TimesFMPreTrainedModel):
         output_attentions: bool | None = None,
         output_hidden_states: bool | None = None,
         return_dict: bool | None = None,
-    ) -> TimesFMOutputForPrediction:
+    ) -> TimesFMOutputForPrediction | tuple[torch.Tensor, ...]:
         """Forecasts on a list of time series.
 
         Args:
@@ -887,15 +900,15 @@ class TimesFMModelForPrediction(TimesFMPreTrainedModel):
             when available, i.e. after the first input patch.
           truncate_negative: truncate to only non-negative values if all the contexts
             have non-negative values.
+          output_attentions: Whether to return the attentions.
+          output_hidden_states: Whether to return the hidden states.
+          return_dict: Whether to return a TimesFMOutputForPrediction object.
 
         Returns:
-        A tuple for Tensors:
-        - the mean forecast of size (# inputs, # forecast horizon),
-        - the full forecast (mean + quantiles) of size
+          A TimesFMOutputForPrediction object containing:
+          - the mean forecast of size (# inputs, # forecast horizon),
+          - the full forecast (mean + quantiles) of size
             (# inputs,  # forecast horizon, 1 + # quantiles).
-
-        Raises:
-        ValueError: If the checkpoint is not properly loaded.
         """
         if return_dict is None:
             return_dict = self.config.use_return_dict