diff --git a/torchvision/prototype/ops/__init__.py b/torchvision/prototype/ops/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/torchvision/prototype/ops/_utils.py b/torchvision/prototype/ops/_utils.py
new file mode 100644
index 00000000000..037d90fd304
--- /dev/null
+++ b/torchvision/prototype/ops/_utils.py
@@ -0,0 +1,138 @@
+import copy
+import operator
+import warnings
+from typing import Callable, Optional, Tuple, Union
+
+import torch
+from torch import fx
+from torchvision.models.feature_extraction import LeafModuleAwareTracer
+
+
+# TODO: Investigate what happens in the scenario of y = x + f1(x) + f2(x).
+
+
+class RegularizedShortcut(torch.nn.Module):
+    def __init__(self, regularizer_layer: Callable[..., torch.nn.Module]):
+        super().__init__()
+        self._regularizer = regularizer_layer()
+
+    def forward(self, input, result):
+        return input + self._regularizer(result)
+
+
+def add_regularized_shortcut(
+    model: torch.nn.Module,
+    block_types: Union[type, Tuple[type, ...]],
+    regularizer_layer: Callable[..., torch.nn.Module],
+    inplace: bool = True,
+) -> torch.nn.Module:
+    if not inplace:
+        model = copy.deepcopy(model)
+
+    reg_name = RegularizedShortcut.__name__.lower()
+    tracer = fx.Tracer()
+    modifications = {}
+    for name, m in model.named_modules():
+        if isinstance(m, block_types):
+            # Add the Layer directly on submodule prior tracing
+            # workaround due to https://github.com/pytorch/pytorch/issues/66197
+            m.add_module(reg_name, RegularizedShortcut(regularizer_layer))
+
+            graph = tracer.trace(m)
+            patterns = {operator.add, torch.add, "add"}
+
+            input = None
+            for node in graph.nodes:
+                if node.op == "call_function":
+                    if node.target in patterns and len(node.args) == 2 and input in node.args:
+                        # TODO: ensure the arg2 has "input" as its ancestor
+                        with graph.inserting_after(node):
+                            # Always put the shortcut value first
+                            args = node.args if node.args[0] == input else node.args[::-1]
+                            node.replace_all_uses_with(graph.call_module(reg_name, args))
+                        graph.erase_node(node)
+                        modifications[name] = graph
+                        break
+                elif node.op == "placeholder":
+                    input = node
+
+    if modifications:
+        # Update the model by overwriting its modules
+        for name, graph in modifications.items():
+            graph.lint()
+            parent_name, child_name = name.rsplit(".", 1)
+            parent = model.get_submodule(parent_name)
+            previous_child = parent.get_submodule(child_name)
+            new_child = fx.GraphModule(previous_child, graph, previous_child.__class__.__name__)
+            parent.register_module(child_name, new_child)
+    else:
+        warnings.warn(
+            "No shortcut was detected. Please ensure you have provided the correct `block_types` parameter "
+            "for this model."
+        )
+
+    return model
+
+
+def del_regularized_shortcut(
+    model: torch.nn.Module,
+    block_types: Union[type, Tuple[type, ...]] = RegularizedShortcut,
+    op: Optional[Callable] = operator.add,
+    inplace: bool = True,
+) -> torch.nn.Module:
+    if isinstance(block_types, type):
+        block_types = (block_types,)
+    if not inplace:
+        model = copy.deepcopy(model)
+
+    tracer = LeafModuleAwareTracer(leaf_modules=block_types)
+    graph = tracer.trace(model)
+    for node in graph.nodes:
+        # The isinstance() won't work if the model has already been traced before because it loses
+        # the class info of submodules. See https://github.com/pytorch/pytorch/issues/66335
+        if node.op == "call_module" and isinstance(model.get_submodule(node.target), block_types):
+            if op is not None:
+                with graph.inserting_before(node):
+                    new_node = graph.call_function(op, node.args)
+                    node.replace_all_uses_with(new_node)
+            else:
+                if len(node.args) == 1:
+                    node.replace_all_uses_with(node.prev)
+                else:
+                    raise ValueError("Can't eliminate an operator that receives more than 1 arguments.")
+            graph.erase_node(node)
+
+    return fx.GraphModule(model, graph, model.__class__.__name__)
+
+
+if __name__ == "__main__":
+    from functools import partial
+
+    from torchvision.models.resnet import resnet50, BasicBlock, Bottleneck
+    from torchvision.ops.stochastic_depth import StochasticDepth
+
+    out = []
+    batch = torch.randn((7, 3, 224, 224))
+
+    print("Before")
+    model = resnet50()
+    with torch.no_grad():
+        out.append(model(batch))
+    fx.symbolic_trace(model).graph.print_tabular()
+
+    print("After addition")
+    regularizer_layer = partial(StochasticDepth, p=0.0, mode="row")
+    model = add_regularized_shortcut(model, (BasicBlock, Bottleneck), regularizer_layer)
+    fx.symbolic_trace(model).graph.print_tabular()
+    # print(model)
+    with torch.no_grad():
+        out.append(model(batch))
+
+    print("After deletion")
+    model = del_regularized_shortcut(model)
+    fx.symbolic_trace(model).graph.print_tabular()
+    with torch.no_grad():
+        out.append(model(batch))
+
+    for v in out[1:]:
+        torch.testing.assert_allclose(out[0], v)