Fix lint

Author: pmcollins

docs/examples/patch_leaf_subclasses/langchain_example.py

@@ -1,6 +1,6 @@
 import argparse
 
-from langchain_core.language_models import BaseLLM, BaseChatModel
+from langchain_core.language_models import BaseChatModel, BaseLLM
 from langchain_core.language_models.base import BaseLanguageModel
 
 # important note: if you import these after patching, the patch won't apply!
@@ -14,19 +14,27 @@
 
 def parse_args():
     parser = argparse.ArgumentParser(description="LangChain model comparison")
-    parser.add_argument("--provider", choices=["ollama", "openai", "huggingface"], default="ollama",
-                        help="Choose model provider (default: ollama)")
+    parser.add_argument(
+        "--provider",
+        choices=["ollama", "openai", "huggingface"],
+        default="ollama",
+        help="Choose model provider (default: ollama)",
+    )
     parser.add_argument("--model", type=str, help="Specify model name")
-    parser.add_argument("--prompt", type=str, default="What is the capital of France?",
-                        help="Input prompt")
+    parser.add_argument(
+        "--prompt",
+        type=str,
+        default="What is the capital of France?",
+        help="Input prompt",
+    )
 
     return parser.parse_args()
 
 
 def chat_with_model(model: BaseLanguageModel, prompt: str) -> str:
     try:
         response = model.invoke(prompt)
-        if hasattr(response, 'content'):
+        if hasattr(response, "content"):
             return response.content
         else:
             return str(response)
@@ -35,24 +43,15 @@ def chat_with_model(model: BaseLanguageModel, prompt: str) -> str:
 
 
 def create_huggingface_model(model: str = "google/flan-t5-small"):
-    return HuggingFaceEndpoint(
-        repo_id=model,
-        temperature=0.7
-    )
+    return HuggingFaceEndpoint(repo_id=model, temperature=0.7)
 
 
 def create_openai_model(model: str = "gpt-3.5-turbo"):
-    return ChatOpenAI(
-        model=model,
-        temperature=0.7
-    )
+    return ChatOpenAI(model=model, temperature=0.7)
 
 
 def create_ollama_model(model: str = "llama2"):
-    return OllamaLLM(
-        model=model,
-        temperature=0.7
-    )
+    return OllamaLLM(model=model, temperature=0.7)
 
 
 def patch_llm():

docs/examples/patch_leaf_subclasses/simple_example.py

@@ -19,7 +19,8 @@ def greet(self):
         print("hola")
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
+
     def my_wrapper(orig_fcn):
         def wrapped_fcn(self, *args, **kwargs):
             print("wrapper running")
@@ -28,7 +29,6 @@ def wrapped_fcn(self, *args, **kwargs):
 
         return wrapped_fcn
 
-
     patch_leaf_subclasses(Greeter, "greet", my_wrapper)
 
     EngishGreeter().greet()

opentelemetry-api/src/opentelemetry/util/_patch.py

@@ -14,7 +14,9 @@ def patch_leaf_subclasses(base_class, method_name, wrapper):
     for subclass in leaf_subclasses:
         # Patch if the subclass has the method (either defined or inherited)
         # and it's actually callable
-        if hasattr(subclass, method_name) and callable(getattr(subclass, method_name)):
+        if hasattr(subclass, method_name) and callable(
+            getattr(subclass, method_name)
+        ):
             old_method = getattr(subclass, method_name)
             setattr(subclass, method_name, wrapper(old_method))