port-model: add 7 typed port examples as eval baseline

Summary:
Adds seven Tensor[]-shaped ports under fbcode/pyrefly/test/tensor_shapes/models/:
mobilenetv2, mixtral_moe, wavernn, openpose, apg, finalmlp, masknet.

Each was selected as the highest-scoring round from the v7 multi-round
eval batch (R1 blind → R2 self-reflection → optional R3) using the
verify_port.sh metric (shaped-vs-bare ratio, receipt coverage, smoke
tests). They serve two purposes:

1. Reference ports demonstrating the skill's intended output across
   architectures (vision, MoE transformer, RNN, pose estimation,
   recommendation MLPs).
2. A baseline corpus future skill iterations can be evaluated against
   without re-burning compute on the v7 multi-round flow.

Also lifts three stub additions surfaced by the mobilenetv2 port into
fixtures/torch/nn/__init__.pyi: Conv2d.bias, GroupNorm.weight,
GroupNorm.bias. Without these, the init-loop pattern
(`for m in self.modules(): if isinstance(m, nn.Conv2d): m.bias`) fails
to typecheck.

Reviewed By: stroxler

Differential Revision: D102635653

fbshipit-source-id: 1508cc0bffd34726a22a3706c84e792890ad91e0

Author: avikchaudhuri

test/tensor_shapes/fixtures/torch/nn/__init__.pyi

@@ -312,6 +312,10 @@ class RMSNorm(Module):
 
 class GroupNorm(Module):
     """Applies Group Normalization"""
+
+    weight: Tensor
+    bias: Tensor
+
     def __init__(
         self,
         num_groups: int,
@@ -503,6 +507,7 @@ class Conv2d[InC, OutC, K, S = 1, P = 0, D = 1](Module):
     """
 
     weight: Tensor[OutC, InC, K, K]
+    bias: Tensor[OutC] | None
 
     def __init__(
         self,