Refactor: Remove unused 'responses' argument from Gemma3 generate_preprocess

keras_hub/src/models/gemma3/gemma3_causal_lm_preprocessor.py

@@ -682,30 +682,25 @@ def generate_preprocess(
         if isinstance(x, dict):
             images = x.get("images", None)
 
-            # TODO: do we even need `responses` for generation? Makes sense for
-            # finetuning only (i.e., `call()`).
-            responses = x.get("responses", None)
             prompts = x["prompts"]
         else:
             images = None
-            responses = None
+
             prompts = x
 
         # Find out if the input is batched/not batched. Uprank if not batched.
         # In other preprocessors, we don't have to do this, but here, all
         # the following logic (indices, etc.) uses tensors with a batch dim.
         # We will squeeze these back at the end.
+        batched = True
+
         batched = True
         if isinstance(prompts, str):
             batched = False
             prompts = [prompts]
-            if responses is not None:
-                responses = [responses]
         if isinstance(prompts, tf.Tensor) and len(prompts.shape) == 0:
             batched = False
             prompts = tf.expand_dims(prompts, axis=0)
-            if responses is not None:
-                responses = tf.expand_dims(responses, axis=0)
 
         # We have the same 8 cases here, as in `call()`.
         if self.text_only_model and images is not None:
@@ -729,11 +724,7 @@ def generate_preprocess(
         # === Tokenization, padding, etc. ===
         prompts = self.tokenizer(prompts)
 
-        if responses is not None:
-            responses = self.tokenizer(responses)
-            segments = (prompts, responses)
-        else:
-            segments = (prompts,)
+        segments = (prompts,)
 
         # Padding.
         token_ids, segment_ids = self.packer(