feat: Add open ai compatible moderations api

llama_stack/providers/inline/safety/llama_guard/llama_guard.py

@@ -260,45 +260,6 @@ class LlamaGuardSafetyImpl(Safety, ShieldsProtocolPrivate):
         return await impl.run_create(messages)
 
 
-async def create(
-    self,
-    input: str | list[str],
-    model: str | None = None,  # To replace with default model for llama-guard
-) -> ModerationObject:
-    if model is None:
-        raise ValueError("Model cannot be None")
-    if not input or len(input) == 0:
-        raise ValueError("Input cannot be empty")
-    if isinstance(input, list):
-        messages = input.copy()
-    else:
-        messages = [input]
-
-    # convert to user messages format with role
-    messages = [UserMessage(content=m) for m in messages]
-
-    # Use the inference API's model resolution instead of hardcoded mappings
-    # This allows the shield to work with any registered model
-    # Determine safety categories based on the model type
-    # For known Llama Guard models, use specific categories
-    if model in LLAMA_GUARD_MODEL_IDS:
-        # Use the mapped model for categories but the original model_id for inference
-        mapped_model = LLAMA_GUARD_MODEL_IDS[model]
-        safety_categories = MODEL_TO_SAFETY_CATEGORIES_MAP.get(mapped_model, DEFAULT_LG_V3_SAFETY_CATEGORIES)
-    else:
-        # For unknown models, use default Llama Guard 3 8B categories
-        safety_categories = DEFAULT_LG_V3_SAFETY_CATEGORIES + [CAT_CODE_INTERPRETER_ABUSE]
-
-    impl = LlamaGuardShield(
-        model=model,
-        inference_api=self.inference_api,
-        excluded_categories=self.config.excluded_categories,
-        safety_categories=safety_categories,
-    )
-
-    return await impl.run_create(messages)
-
-
 class LlamaGuardShield:
     def __init__(
         self,