[test] merge old components to test to model zoo (#4945)

* [test] add custom models in model zoo

* [test] update legacy test

* [test] update model zoo

* [test] update gemini test

* [test] remove components to test

tests/kit/model_zoo/custom/nested_model.py

@@ -0,0 +1,53 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ..registry import model_zoo
+from .base import CheckpointModule
+
+
+class SubNet(nn.Module):
+    def __init__(self, out_features) -> None:
+        super().__init__()
+        self.bias = nn.Parameter(torch.zeros(out_features))
+
+    def forward(self, x, weight):
+        return F.linear(x, weight, self.bias)
+
+
+class NestedNet(CheckpointModule):
+    def __init__(self, checkpoint=False) -> None:
+        super().__init__(checkpoint)
+        self.fc1 = nn.Linear(5, 5)
+        self.sub_fc = SubNet(5)
+        self.fc2 = nn.Linear(5, 2)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.sub_fc(x, self.fc1.weight)
+        x = self.fc1(x)
+        x = self.fc2(x)
+        return x
+
+
+def data_gen():
+    return dict(x=torch.rand(16, 5))
+
+
+def loss_fn(x):
+    outputs = x["x"]
+    label = torch.randint(low=0, high=2, size=(16,), device=outputs.device)
+    return F.cross_entropy(x["x"], label)
+
+
+def output_transform(x: torch.Tensor):
+    return dict(x=x)
+
+
+model_zoo.register(
+    name="custom_nested_model",
+    model_fn=NestedNet,
+    data_gen_fn=data_gen,
+    output_transform_fn=output_transform,
+    loss_fn=loss_fn,
+)