code: def test_track_pytorch_model():
      pytest.importorskip("torch")
  
      class Net(nn.Module):
          def __init__(self):
              super().__init__()
              self.conv1 = nn.Conv2d(1, 32, 3, 1)
              self.conv2 = nn.Conv2d(32, 64, 3, 1)
              self.dropout1 = nn.Dropout(0.25)
              self.dropout2 = nn.Dropout(0.5)
              self.fc1 = nn.Linear(9216, 128)
              self.fc2 = nn.Linear(128, 10)
  
          def forward(self, x):
              x = self.conv1(x)
              x = F.relu(x)
              x = self.conv2(x)
              x = F.relu(x)
              x = F.max_pool2d(x, 2)
              x = self.dropout1(x)
              x = torch.flatten(x, 1)
              x = self.fc1(x)
              x = F.relu(x)
              x = self.dropout2(x)
              x = self.fc2(x)
              output = F.log_softmax(x, dim=1)
              return output
  
      model = Net()
      assert (
          wandb_track("model", model, testing=True, models=True)
          == "nn.Module"
      )
      assert (
          wandb_track("model", model, testing=True, models=False)
          is None
      )
  
file_path: wandb/tests/pytest_tests/unit_tests/test_metaflow.py
imports: import platform
  from pathlib import Path
  import pytest
  import pandas
  from metaflow import FlowSpec, step
  from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier
  from wandb.integration.metaflow import wandb_log, wandb_track, wandb_use
label_desc: Use image node_id 7 for calling a global function with example usage: test_track_pytorch_model() and returns: output