diff --git a/src/chronos/chronos.py b/src/chronos/chronos.py
index 31272b5..e14f963 100644
--- a/src/chronos/chronos.py
+++ b/src/chronos/chronos.py
@@ -351,7 +351,7 @@ def _prepare_and_validate_context(
     @torch.no_grad()
     def embed(
         self, context: Union[torch.Tensor, List[torch.Tensor]]
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
+    ) -> Tuple[torch.Tensor, Any]:
         """
         Get encoder embeddings for the given time series.
 
@@ -365,21 +365,23 @@ def embed(
 
         Returns
         -------
-        embeddings, scale
-            A tuple of two tensors: the encoder embeddings and the time series scale.
+        embeddings, decoding_context
+            A tuple of two tensors: the encoder embeddings and the decoding_context,
+            e.g., the scale of the time series in the case of mean scaling.
             The encoder embeddings are shaped (batch_size, context_length, d_model)
             or (batch_size, context_length + 1, d_model), where the extra 1 is for EOS.
             If your original time series were shorter than the model's context_length,
             please slice the returned embeddings along the time axis accordingly.
-            The scale is shaped (batch_size, ).
         """
         context = self._prepare_and_validate_context(context=context)
-        token_ids, attention_mask, scale = self.tokenizer.input_transform(context)
+        token_ids, attention_mask, decoding_context = self.tokenizer.input_transform(
+            context
+        )
         embeddings = self.model.encode(
             input_ids=token_ids.to(self.model.device),
             attention_mask=attention_mask.to(self.model.device),
         ).cpu()
-        return embeddings, scale
+        return embeddings, decoding_context
 
     def predict(
         self,