Fix docstring and import

keras_core/backend/tensorflow/tf_utils.py

@@ -110,6 +110,7 @@ def encode_categorical_inputs(
     else:
         return tf.multiply(bincounts, idf_weights)
 
+
 def get_tensor_spec(t, dynamic_batch=False, name=None):
     """Returns a `TensorSpec` given a single `Tensor` or `TensorSpec`."""
     if isinstance(t, tf.TypeSpec):

keras_core/export/export_lib.py

@@ -1,12 +1,11 @@
 """Library for exporting inference-only Keras models/layers."""
 
-import tensorflow as tf
-
 from keras_core import backend
 from keras_core.api_export import keras_core_export
 from keras_core.layers import Layer
 from keras_core.models import Functional
 from keras_core.models import Sequential
+from keras_core.module_utils import tensorflow as tf
 from keras_core.utils import io_utils
 
 

keras_core/layers/preprocessing/hashing.py

@@ -2,9 +2,9 @@
 
 from keras_core import backend
 from keras_core.api_export import keras_core_export
+from keras_core.backend.tensorflow import tf_utils
 from keras_core.layers.layer import Layer
 from keras_core.utils import backend_utils
-from keras_core.backend.tensorflow import tf_utils
 from keras_core.utils.module_utils import tensorflow as tf
 
 

keras_core/models/model.py

@@ -448,7 +448,10 @@ def to_json(self, **kwargs):
         return json.dumps(model_config, **kwargs)
 
     def export(self, filepath, format="tf_saved_model"):
-        """Create a SavedModel artifact for inference (e.g. via TF-Serving).
+        """[TF backend only]* Create a TF SavedModel artifact for inference
+        (e.g. via TF-Serving).
+
+        *Note: This can currently only be used with the TF backend.
 
         This method lets you export a model to a lightweight SavedModel artifact
         that contains the model's forward pass only (its `call()` method)