Update nn.Sequential signature to match PyTorch (*modules varargs) (#2827)

`nn.Sequential.__init__` accepted a `list[Module] | None` parameter,
diverging from PyTorch's `torch.nn.Sequential` which takes variadic
positional args.

## Changes

- **`nn/_sequential.py`**: Added `__init__(self, *modules: Module)`
override that accepts variadic args and delegates to
`ModuleList.__init__`; updated docstring example.
- **`nn/_module_test.py`**: Migrated all `Sequential([...])` call sites
to `Sequential(...)`.

## Before / After

```python
# Before
seq = Sequential([SiLU(), Linear(4, 4)])
seq_empty = Sequential([])

# After — matches torch.nn.Sequential
seq = Sequential(SiLU(), Linear(4, 4))
seq_empty = Sequential()
```

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<details>

<summary>Original prompt</summary>

> 
> ----
> 
> *This section details on the original issue you should resolve*
> 
> <issue_title>Update nn.Sequential signature to match
pytorch</issue_title>
> <issue_description>`def __init__(self, modules: list[Module] | None =
None) -> None` should become `def __init__(self, *modules: Module =
None) -> None:` to match pytorch signature.</issue_description>
> 
> ## Comments on the Issue (you are @copilot in this section)
> 
> <comments>
> </comments>
> 


</details>



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- Fixes #2826

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Author: Copilot

onnxscript/nn/_module_test.py

@@ -854,7 +854,7 @@ def forward(self, op, x):
                 return op.Add(x, op.Constant(value_float=1.0))
 
         graph, op, x = self._make_input()
-        seq = Sequential([AddOne(), AddOne(), AddOne()])
+        seq = Sequential(AddOne(), AddOne(), AddOne())
         result = seq(op, x)
 
         self.assertIsInstance(result, ir.Value)
@@ -869,7 +869,7 @@ def forward(self, op, x):
                 return op.Identity(x)
 
         _, op, x = self._make_input()
-        seq = Sequential([PassThrough()])
+        seq = Sequential(PassThrough())
         result = seq(op, x)
         self.assertIsInstance(result, ir.Value)
 
@@ -899,7 +899,7 @@ def forward(self, op, pair):
                 return op.Add(a, b)
 
         graph, op, x = self._make_input()
-        seq = Sequential([SplitTwo(), UnpackAndAdd()])
+        seq = Sequential(SplitTwo(), UnpackAndAdd())
         result = seq(op, x)
 
         self.assertIsInstance(result, ir.Value)
@@ -920,7 +920,7 @@ def forward(self, op, pair):
                 return op.Add(a, b)
 
         _, op, x = self._make_input()
-        seq = Sequential([SplitTwoList(), UnpackAndAdd()])
+        seq = Sequential(SplitTwoList(), UnpackAndAdd())
         result = seq(op, x)
         self.assertIsInstance(result, ir.Value)
 
@@ -938,7 +938,7 @@ def forward(self, op, pair):
                 return pair
 
         _, op, x = self._make_input()
-        seq = Sequential([ReturnPair(), TupleIdentity()])
+        seq = Sequential(ReturnPair(), TupleIdentity())
         result = seq(op, x)
         self.assertIsInstance(result, tuple)
         self.assertEqual(len(result), 2)
@@ -951,7 +951,7 @@ def forward(self, op, x):
                 return (op.Identity(x), op.Identity(x))
 
         _, op, x = self._make_input()
-        seq = Sequential([ReturnPair()])
+        seq = Sequential(ReturnPair())
         result = seq(op, x)
         self.assertIsInstance(result, tuple)
         self.assertEqual(len(result), 2)
@@ -968,7 +968,7 @@ def forward(self, op, x):
                 return (op.Identity(x), op.Identity(x), op.Identity(x))
 
         _, op, x = self._make_input()
-        seq = Sequential([Identity(), SplitThree()])
+        seq = Sequential(Identity(), SplitThree())
         result = seq(op, x)
         self.assertIsInstance(result, tuple)
         self.assertEqual(len(result), 3)
@@ -987,7 +987,7 @@ def forward(self, op, x):
 
         _, op = _create_graph_and_op()
         accept = AcceptNone()
-        seq = Sequential([ReturnNone(), accept])
+        seq = Sequential(ReturnNone(), accept)
         result = seq(op, "anything")
         self.assertIsNone(result)
         self.assertIsNone(accept.received)
@@ -1008,7 +1008,7 @@ def forward(self, op, x):
         class Model(Module):
             def __init__(self):
                 super().__init__("model")
-                self.layers = Sequential([Linear(4, 4), Linear(4, 4)])
+                self.layers = Sequential(Linear(4, 4), Linear(4, 4))
 
             def forward(self, op, x):
                 return self.layers(op, x)
@@ -1035,7 +1035,7 @@ def __init__(self, size):
             def forward(self, op, x):
                 return op.MatMul(x, op.Transpose(self.weight, perm=[1, 0]))
 
-        seq = Sequential([SiLU(), Linear(4)])
+        seq = Sequential(SiLU(), Linear(4))
         named = dict(seq.named_parameters())
         # SiLU at index 0 has no params; Linear at index 1 has weight
         self.assertIn("1.weight", named)
@@ -1061,7 +1061,7 @@ def forward(self, op, x):
         class Model(Module):
             def __init__(self):
                 super().__init__("model")
-                self.blocks = Sequential([])
+                self.blocks = Sequential()
                 # Append AFTER __setattr__ has set Sequential._name = "blocks"
                 self.blocks.append(Linear(4))
                 self.blocks.append(Linear(4))

onnxscript/nn/_sequential.py

@@ -24,13 +24,16 @@ def forward(self, op, x):
 
         # Produces parameter names: "mod.0.weight", "mod.0.bias"
         # SiLU at index 0 has no parameters.
-        mod = Sequential([SiLU(), Linear(4, 4)])
+        mod = Sequential(SiLU(), Linear(4, 4))
 
         # Calling mod(op, x) is equivalent to:
         #   x = silu(op, x)
         #   x = linear(op, x)
     """
 
+    def __init__(self, *modules: _module_list.Module) -> None:
+        super().__init__(modules)
+
     def _set_name(self, name: str) -> None:
         """Set this container's name. Children keep simple ``"0"``, ``"1"`` names.