to revert

optimum/exporters/tflite/base.py

@@ -265,9 +265,65 @@ def _create_dummy_input_generator_classes(self) -> List["DummyInputGenerator"]:
         self._validate_mandatory_axes()
         return [cls_(self.task, self._normalized_config, **self._axes) for cls_ in self.DUMMY_INPUT_GENERATOR_CLASSES]
 
+
+    @property
+    def values_override(self) -> Optional[Dict[str, Any]]:
+        """
+        Dictionary of keys to override in the model's config before exporting.
+        Returns:
+            `Optional[Dict[str, Any]]`: A dictionary specifying the configuration items to override.
+        """
+        if hasattr(self._config, "use_cache"):
+            return {"use_cache": False}
+
+        return None
+
+    @property
+    @abstractmethod
+    def inputs(self) -> List[str]:
+        """
+        List containing the names of the inputs the exported model should take.
+        Returns:
+            `List[str]`: A list of input names.
+        """
+        raise NotImplementedError()
+
+    @property
+    def outputs(self) -> List[str]:
+        """
+        List containing the names of the outputs the exported model should have.
+        Returns:
+            `List[str]`: A list of output names.
+        """
+        return self._TASK_TO_COMMON_OUTPUTS[self.task]
+
+
     def generate_dummy_inputs(self) -> Dict[str, "tf.Tensor"]:
-        return super().generate_dummy_inputs(framework="tf")
+        """
+        Generates dummy inputs that the exported model should be able to process.
+        This method is actually used to determine the input specs that are needed for the export.
+        Returns:
+            `Dict[str, tf.Tensor]`: A dictionary mapping input names to dummy tensors.
+        """
+        dummy_inputs_generators = self._create_dummy_input_generator_classes()
+        dummy_inputs = {}
+
+        for input_name in self.inputs:
+            input_was_inserted = False
+            for dummy_input_gen in dummy_inputs_generators:
+                if dummy_input_gen.supports_input(input_name):
+                    dummy_inputs[input_name] = dummy_input_gen.generate(input_name, framework="tf")
+                    input_was_inserted = True
+                    break
+            if not input_was_inserted:
+                raise RuntimeError(
+                    f'Could not generate dummy inputs for "{input_name}". Try adding a proper dummy input generator '
+                    "to the model TFLite config."
+                )
 
+        return dummy_inputs
+
+
     @property
     def inputs_specs(self) -> List["TensorSpec"]:
         """