Fix type-checking issues (#295)

*Issue #, if available:* See example build
https://github.com/amazon-science/chronos-forecasting/actions/runs/14302765904/job/40313421985

*Description of changes:*
- Address type-checker complaints, where possible
- Bump bugfix version of the package


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pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "chronos-forecasting"
-version = "1.5.0"
+version = "1.5.1"
 authors = [
   { name="Abdul Fatir Ansari", email="ansarnd@amazon.com" },
   { name="Lorenzo Stella", email="stellalo@amazon.com" },

src/chronos/chronos.py

@@ -305,6 +305,8 @@ class ChronosModel(nn.Module):
         assert (
             self.config.model_type == "seq2seq"
         ), "Encoder embeddings are only supported for encoder-decoder models"
+        assert hasattr(self.model, "encoder")
+
         return self.model.encoder(
             input_ids=input_ids, attention_mask=attention_mask
         ).last_hidden_state
@@ -344,6 +346,8 @@ class ChronosModel(nn.Module):
         if top_p is None:
             top_p = self.config.top_p
 
+        assert hasattr(self.model, "generate")
+
         preds = self.model.generate(
             input_ids=input_ids,
             attention_mask=attention_mask,

src/chronos/chronos_bolt.py

@@ -136,12 +136,12 @@ class ResidualBlock(nn.Module):
 
 
 class ChronosBoltModelForForecasting(T5PreTrainedModel):
-    _keys_to_ignore_on_load_missing = [
+    _keys_to_ignore_on_load_missing = [  # type: ignore
         r"input_patch_embedding\.",
         r"output_patch_embedding\.",
     ]
-    _keys_to_ignore_on_load_unexpected = [r"lm_head.weight"]
-    _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
+    _keys_to_ignore_on_load_unexpected = [r"lm_head.weight"]  # type: ignore
+    _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]  # type: ignore
 
     def __init__(self, config: T5Config):
         assert hasattr(config, "chronos_config"), "Not a Chronos config file"
@@ -358,7 +358,7 @@ class ChronosBoltModelForForecasting(T5PreTrainedModel):
                     (target - quantile_preds)
                     * (
                         (target <= quantile_preds).float()
-                        - self.quantiles.view(1, self.num_quantiles, 1)
+                        - self.quantiles.view(1, self.num_quantiles, 1)  # type: ignore
                     )
                 )
                 * target_mask.float()
@@ -429,7 +429,7 @@ class ChronosBoltPipeline(BaseChronosPipeline):
     default_context_length: int = 2048
 
     def __init__(self, model: ChronosBoltModelForForecasting):
-        super().__init__(inner_model=model)
+        super().__init__(inner_model=model)  # type: ignore
         self.model = model
 
     @property