fix linting

examples/lightrag_gemini_demo_no_tiktoken.py

@@ -51,10 +51,12 @@ class GemmaTokenizer(Tokenizer):
         "google/gemma3": _TokenizerConfig(
             tokenizer_model_url="https://raw.githubusercontent.com/google/gemma_pytorch/cb7c0152a369e43908e769eb09e1ce6043afe084/tokenizer/gemma3_cleaned_262144_v2.spiece.model",
             tokenizer_model_hash="1299c11d7cf632ef3b4e11937501358ada021bbdf7c47638d13c0ee982f2e79c",
-        )
-    }            
+        ),
+    }
 
-    def __init__(self, model_name: str = "gemini-2.0-flash", tokenizer_dir: Optional[str] = None):
+    def __init__(
+        self, model_name: str = "gemini-2.0-flash", tokenizer_dir: Optional[str] = None
+    ):
         # https://github.com/google/gemma_pytorch/tree/main/tokenizer
         if "1.5" in model_name or "1.0" in model_name:
             # up to gemini 1.5 gemma2 is a comparable local tokenizer
@@ -77,7 +79,9 @@ class GemmaTokenizer(Tokenizer):
         else:
             model_data = None
         if not model_data:
-            model_data = self._load_from_url(file_url=file_url, expected_hash=expected_hash)
+            model_data = self._load_from_url(
+                file_url=file_url, expected_hash=expected_hash
+            )
             self.save_tokenizer_to_cache(cache_path=file_path, model_data=model_data)
 
         tokenizer = spm.SentencePieceProcessor()
@@ -140,7 +144,7 @@ class GemmaTokenizer(Tokenizer):
 
     # def encode(self, content: str) -> list[int]:
     #     return self.tokenizer.encode(content)
-        
+
     # def decode(self, tokens: list[int]) -> str:
     #     return self.tokenizer.decode(tokens)
 
@@ -187,7 +191,10 @@ async def initialize_rag():
     rag = LightRAG(
         working_dir=WORKING_DIR,
         # tiktoken_model_name="gpt-4o-mini",
-        tokenizer=GemmaTokenizer(tokenizer_dir=(Path(WORKING_DIR) / "vertexai_tokenizer_model"), model_name="gemini-2.0-flash"),
+        tokenizer=GemmaTokenizer(
+            tokenizer_dir=(Path(WORKING_DIR) / "vertexai_tokenizer_model"),
+            model_name="gemini-2.0-flash",
+        ),
         llm_model_func=llm_model_func,
         embedding_func=EmbeddingFunc(
             embedding_dim=384,