Fix toxicity comments (#982)

steps/src/toxicity_guardrail/toxicity_guardrail.py

@@ -14,21 +14,9 @@
 #
 
 from typing import Any, Dict
-
+from transformers import pipeline
 
 class ToxicityGuardrailStep:
-    """
-    A serving graph step that filters out toxic requests using a pre-trained
-    text classification model.
-
-    If the toxicity score of the input text meets or exceeds the threshold,
-    the request is blocked with a ValueError. Safe requests are passed through
-    unchanged.
-
-    The classifier label "toxic" maps directly to the toxicity score; any
-    other label (e.g. "non-toxic") inverts the model's confidence score.
-    """
-
     def __init__(
         self,
         context=None,
@@ -37,13 +25,23 @@ def __init__(
         model_name: str = "unitary/toxic-bert",
         **kwargs,
     ):
+        """
+        A serving graph step that filters out toxic requests using a pre-trained
+        text classification model.
+
+        :param context: MLRun context object, injected automatically by the serving graph.
+        :param name: Name of this step in the serving graph.
+        :param threshold: Toxicity score threshold; requests whose toxicity score meets or
+                          exceeds this value are blocked with a ValueError. Defaults to 0.5.
+        :param model_name: HuggingFace model identifier used for text classification.
+                           Defaults to "unitary/toxic-bert".
+        :param kwargs: Additional keyword arguments forwarded to the serving graph step base.
+        """
         self.threshold = threshold
         self.model_name = model_name
         self._classifier = None
 
     def post_init(self, mode="sync", **kwargs):
-        from transformers import pipeline
-
         self._classifier = pipeline("text-classification", model=self.model_name)
 
     def do(self, event: Dict[str, Any]) -> Dict[str, Any]: