diff --git a/src/bentoml/_internal/frameworks/tensorflow_v2.py b/src/bentoml/_internal/frameworks/tensorflow_v2.py
index 2bfab21845d..3f603c7fe5d 100644
--- a/src/bentoml/_internal/frameworks/tensorflow_v2.py
+++ b/src/bentoml/_internal/frameworks/tensorflow_v2.py
@@ -35,11 +35,12 @@
     from ..external_typing import tensorflow as tf_ext
 
     TFArgType = t.Union[t.List[t.Union[int, float]], ext.NpNDArray, tf_ext.Tensor]
+    TFModelOutputType = tf_ext.EagerTensor | tuple[tf_ext.EagerTensor]
+    TFRunnableOutputType = ext.NpNDArray | tuple[ext.NpNDArray]
+
 
 try:
     import tensorflow as tf
-    TFModelOutputType = tf_ext.EagerTensor | tuple[tf_ext.EagerTensor]
-    TFRunnableOutputType = ext.NpNDArray | tuple[ext.NpNDArray]
 except ImportError:  # pragma: no cover
     raise MissingDependencyException(
         "'tensorflow' is required in order to use module 'bentoml.tensorflow', install tensorflow with 'pip install tensorflow'. For more information, refer to https://www.tensorflow.org/install"
@@ -70,7 +71,7 @@ def get(tag_like: str | Tag) -> bentoml.Model:
 def load_model(
     bento_model: str | Tag | bentoml.Model,
     device_name: str = "/device:CPU:0",
-) -> "tf_ext.AutoTrackable" | "tf_ext.Module":
+) -> tf_ext.AutoTrackable | tf_ext.Module:
     """
     Load a tensorflow model from BentoML local modelstore with given name.
 
@@ -343,7 +344,7 @@ def _run_method(
 
     def add_run_method(method_name: str, options: ModelSignature):
         def run_method(
-            runnable_self: TensorflowRunnable, *args: "TFArgType", **kwargs: "TFArgType"
+            runnable_self: TensorflowRunnable, *args: TFArgType, **kwargs: TFArgType
         ) -> TFRunnableOutputType:
             _run_method = runnable_self.methods_cache.get(
                 method_name
@@ -374,9 +375,9 @@ class TensorflowTensorContainer(
 ):
     @classmethod
     def batches_to_batch(
-        cls, batches: t.Sequence["tf_ext.EagerTensor"], batch_dim: int = 0
-    ) -> t.Tuple["tf_ext.EagerTensor", list[int]]:
-        batch: "tf_ext.EagerTensor" = tf.concat(batches, axis=batch_dim)
+        cls, batches: t.Sequence[tf_ext.EagerTensor], batch_dim: int = 0
+    ) -> t.Tuple[tf_ext.EagerTensor, list[int]]:
+        batch: tf_ext.EagerTensor = tf.concat(batches, axis=batch_dim)
         # TODO: fix typing mismatch @larme
         indices: list[int] = list(
             itertools.accumulate(subbatch.shape[batch_dim] for subbatch in batches)
@@ -386,15 +387,15 @@ def batches_to_batch(
 
     @classmethod
     def batch_to_batches(
-        cls, batch: "tf_ext.EagerTensor", indices: t.Sequence[int], batch_dim: int = 0
-    ) -> t.List["tf_ext.EagerTensor"]:
+        cls, batch: tf_ext.EagerTensor, indices: t.Sequence[int], batch_dim: int = 0
+    ) -> t.List[tf_ext.EagerTensor]:
         size_splits = [indices[i + 1] - indices[i] for i in range(len(indices) - 1)]
         return tf.split(batch, size_splits, axis=batch_dim)  # type: ignore
 
     @classmethod
     def to_payload(
         cls,
-        batch: "tf_ext.EagerTensor",
+        batch: tf_ext.EagerTensor,
         batch_dim: int = 0,
     ) -> Payload:
 
@@ -407,14 +408,14 @@ def to_payload(
     def from_payload(
         cls,
         payload: Payload,
-    ) -> "tf_ext.EagerTensor":
+    ) -> tf_ext.EagerTensor:
 
         return pickle.loads(payload.data)
 
     @classmethod
     def batch_to_payloads(
         cls,
-        batch: "tf_ext.EagerTensor",
+        batch: tf_ext.EagerTensor,
         indices: t.Sequence[int],
         batch_dim: int = 0,
     ) -> t.List[Payload]:
@@ -429,7 +430,7 @@ def from_batch_payloads(
         cls,
         payloads: t.Sequence[Payload],
         batch_dim: int = 0,
-    ) -> t.Tuple["tf_ext.EagerTensor", t.List[int]]:
+    ) -> t.Tuple[tf_ext.EagerTensor, t.List[int]]:
         batches = [cls.from_payload(payload) for payload in payloads]
         return cls.batches_to_batch(batches, batch_dim)