code: def __init__(self, device: torch.device) -> None:
      super().__init__()
      # This nested structure exercises traversal order to catch differences
      # between valid traversals (e.g. BFS and DFS variations).
      self.seq1 = nn.Sequential(
          nn.Linear(1, 1, device=device),
          FakeSequential(
              nn.Linear(1, 1, device=device),
              nn.ReLU(),
              FakeSequential(
                  nn.Linear(1, 1, device=device),
              ),
              nn.ReLU(),
          ),
          nn.Linear(1, 2, device=device),
      )
      self.lin = nn.Linear(2, 2, device=device)
      self.seq2 = nn.Sequential(
          nn.ReLU(),
          nn.Linear(2, 3, device=device),
          FakeSequential(
              nn.Linear(3, 2, bias=False, device=device),
              nn.Linear(2, 4, bias=False, device=device),
          ),
      )
  
      def forward(self, x: torch.Tensor) -> torch.Tensor:
          return self.seq2(self.lin(self.seq1(x)))
  
imports: from typing import Tuple
  import torch
  import torch.nn
label_desc: Use image node_id 1 to create a new NestedSequentialModel object from inherited base classes: nn with example: obj = NestedSequentialModel(device)