feat: Add open ai compatible moderations api

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

@@ -221,6 +221,44 @@ class LlamaGuardSafetyImpl(Safety, ShieldsProtocolPrivate):
 
         return await impl.run(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)
+
 
 async def create(
     self,