Remove duplicate export tests in model_test. (#20735)

The same tests exist at:
- https://github.com/keras-team/keras/blob/master/keras/src/export/saved_model_test.py#L66
- https://github.com/keras-team/keras/blob/master/keras/src/export/onnx_test.py#L62

The goal is to isolate the use of `onnxruntime` to a single file, `onnx_test.py`.

keras/src/models/model_test.py

@@ -1219,75 +1219,6 @@ def test_functional_deeply_nested_outputs_struct_losses(self):
         )
         self.assertListEqual(hist_keys, ref_keys)
 
-    @parameterized.named_parameters(
-        ("tf_saved_model", "tf_saved_model"),
-        ("onnx", "onnx"),
-    )
-    @pytest.mark.skipif(
-        backend.backend() not in ("tensorflow", "jax", "torch"),
-        reason=(
-            "Currently, `Model.export` only supports the tensorflow, jax and "
-            "torch backends."
-        ),
-    )
-    @pytest.mark.skipif(
-        testing.jax_uses_gpu(), reason="Leads to core dumps on CI"
-    )
-    def test_export(self, export_format):
-        if export_format == "tf_saved_model" and testing.torch_uses_gpu():
-            self.skipTest("Leads to core dumps on CI")
-
-        temp_filepath = os.path.join(self.get_temp_dir(), "exported_model")
-        model = _get_model()
-        x1 = np.random.rand(1, 3).astype("float32")
-        x2 = np.random.rand(1, 3).astype("float32")
-        ref_output = model([x1, x2])
-
-        model.export(temp_filepath, format=export_format)
-
-        if export_format == "tf_saved_model":
-            import tensorflow as tf
-
-            revived_model = tf.saved_model.load(temp_filepath)
-            self.assertAllClose(ref_output, revived_model.serve([x1, x2]))
-
-            # Test with a different batch size
-            if backend.backend() == "torch":
-                # TODO: Dynamic shape is not supported yet in the torch backend
-                return
-            revived_model.serve(
-                [
-                    np.concatenate([x1, x1], axis=0),
-                    np.concatenate([x2, x2], axis=0),
-                ]
-            )
-        elif export_format == "onnx":
-            import onnxruntime
-
-            ort_session = onnxruntime.InferenceSession(temp_filepath)
-            ort_inputs = {
-                k.name: v for k, v in zip(ort_session.get_inputs(), [x1, x2])
-            }
-            self.assertAllClose(
-                ref_output, ort_session.run(None, ort_inputs)[0]
-            )
-
-            # Test with a different batch size
-            if backend.backend() == "torch":
-                # TODO: Dynamic shape is not supported yet in the torch backend
-                return
-            ort_inputs = {
-                k.name: v
-                for k, v in zip(
-                    ort_session.get_inputs(),
-                    [
-                        np.concatenate([x1, x1], axis=0),
-                        np.concatenate([x2, x2], axis=0),
-                    ],
-                )
-            }
-            ort_session.run(None, ort_inputs)
-
     def test_export_error(self):
         temp_filepath = os.path.join(self.get_temp_dir(), "exported_model")
         model = _get_model()