fix: convert_preprocessing_outputs to safely handle Python-native outputs and prevent .dtype errors. (keras-team#2722)

* fix: convert_preprocessing_outputs to safely handle Python-native outputs and prevent .dtype errors

* fix : robustness

Author: maitry63

keras_hub/src/utils/tensor_utils.py

@@ -207,9 +207,17 @@ def convert_preprocessing_outputs(x):
     def convert(x):
         if x is None:
             return x
-        if isinstance(x, tf.RaggedTensor) or x.dtype == tf.string:
+        if isinstance(x, tf.RaggedTensor):
+            return tensor_to_list(x)
+        dtype = getattr(x, "dtype", None)
+
+        if dtype is None:
+            return x
+
+        if dtype == tf.string:
             return tensor_to_list(x)
-        dtype = keras.backend.standardize_dtype(x.dtype)
+
+        dtype = keras.backend.standardize_dtype(dtype)
         return ops.convert_to_tensor(x, dtype=dtype)
 
     return keras.tree.map_structure(convert, x)

keras_hub/src/utils/tensor_utils_test.py

@@ -85,6 +85,16 @@ def test_composite(self):
         self.assertTrue(is_tensor_type(outputs[1]))
         self.assertTrue(is_tensor_type(outputs[2]))
 
+    def test_python_native_outputs(self):
+        inputs = "hello world"
+        outputs = convert_preprocessing_outputs(inputs)
+        self.assertEqual(outputs, "hello world")
+        self.assertIsInstance(outputs, str)
+        inputs = ["hello", "world"]
+        outputs = convert_preprocessing_outputs(inputs)
+        self.assertEqual(outputs, ["hello", "world"])
+        self.assertIsInstance(outputs, list)
+
         def to_list(x):
             return ops.convert_to_numpy(x).tolist() if is_tensor_type(x) else x