diff --git a/torchvision/models/feature_extraction.py b/torchvision/models/feature_extraction.py
index a6c26913093..8a25e1f7187 100644
--- a/torchvision/models/feature_extraction.py
+++ b/torchvision/models/feature_extraction.py
@@ -184,6 +184,23 @@ def _get_leaf_modules_for_ops() -> List[type]:
     return result
 
 
+def _set_default_tracer_kwargs(original_tr_kwargs: Optional[Dict[str, Any]]) -> Dict[str, Any]:
+    default_autowrap_modules = (math, torchvision.ops)
+    default_leaf_modules = _get_leaf_modules_for_ops()
+    result_tracer_kwargs = {} if original_tr_kwargs is None else original_tr_kwargs
+    result_tracer_kwargs["autowrap_modules"] = (
+        tuple(set(result_tracer_kwargs["autowrap_modules"] + default_autowrap_modules))
+        if "autowrap_modules" in result_tracer_kwargs
+        else default_autowrap_modules
+    )
+    result_tracer_kwargs["leaf_modules"] = (
+        list(set(result_tracer_kwargs["leaf_modules"] + default_leaf_modules))
+        if "leaf_modules" in result_tracer_kwargs
+        else default_leaf_modules
+    )
+    return result_tracer_kwargs
+
+
 def get_graph_node_names(
     model: nn.Module,
     tracer_kwargs: Optional[Dict[str, Any]] = None,
@@ -212,7 +229,11 @@ def get_graph_node_names(
         tracer_kwargs (dict, optional): a dictionary of keywork arguments for
             ``NodePathTracer`` (they are eventually passed onto
             `torch.fx.Tracer <https://pytorch.org/docs/stable/fx.html#torch.fx.Tracer>`_).
-            By default it will be set to wrap and make leaf nodes all torchvision ops.
+            By default it will be set to wrap and make leaf nodes all torchvision ops:
+            {"autowrap_modules": (math, torchvision.ops,),"leaf_modules": _get_leaf_modules_for_ops(),}
+            WARNING: In case the user provides tracer_kwargs, above default arguments will be appended to the user
+            provided dictionary.
+
         suppress_diff_warning (bool, optional): whether to suppress a warning
             when there are discrepancies between the train and eval version of
             the graph. Defaults to False.
@@ -226,14 +247,7 @@ def get_graph_node_names(
         >>> model = torchvision.models.resnet18()
         >>> train_nodes, eval_nodes = get_graph_node_names(model)
     """
-    if tracer_kwargs is None:
-        tracer_kwargs = {
-            "autowrap_modules": (
-                math,
-                torchvision.ops,
-            ),
-            "leaf_modules": _get_leaf_modules_for_ops(),
-        }
+    tracer_kwargs = _set_default_tracer_kwargs(tracer_kwargs)
     is_training = model.training
     train_tracer = NodePathTracer(**tracer_kwargs)
     train_tracer.trace(model.train())
@@ -378,7 +392,10 @@ def create_feature_extractor(
         tracer_kwargs (dict, optional): a dictionary of keywork arguments for
             ``NodePathTracer`` (which passes them onto it's parent class
             `torch.fx.Tracer <https://pytorch.org/docs/stable/fx.html#torch.fx.Tracer>`_).
-            By default it will be set to wrap and make leaf nodes all torchvision ops.
+            By default it will be set to wrap and make leaf nodes all torchvision ops:
+            {"autowrap_modules": (math, torchvision.ops,),"leaf_modules": _get_leaf_modules_for_ops(),}
+            WARNING: In case the user provides tracer_kwargs, above default arguments will be appended to the user
+            provided dictionary.
         suppress_diff_warning (bool, optional): whether to suppress a warning
             when there are discrepancies between the train and eval version of
             the graph. Defaults to False.
@@ -423,14 +440,7 @@ def create_feature_extractor(
         >>>                    'autowrap_functions': [leaf_function]})
 
     """
-    if tracer_kwargs is None:
-        tracer_kwargs = {
-            "autowrap_modules": (
-                math,
-                torchvision.ops,
-            ),
-            "leaf_modules": _get_leaf_modules_for_ops(),
-        }
+    tracer_kwargs = _set_default_tracer_kwargs(tracer_kwargs)
     is_training = model.training
 
     if all(arg is None for arg in [return_nodes, train_return_nodes, eval_return_nodes]):