class_docstr: Final Arch - simple MLP. The output is just one target.
  
  Args:
      in_features (int): the output dimension of the interaction arch.
  
  Example::
  
      B = 20
      over_arch = OverArch()
      logits = over_arch(torch.rand((B, 10)))
class_docstr_tok: ["Final","Arch","-","simple","MLP",".","The","output","is","just","one","target",".","Args",":","in_features","(","int",")",":","the","output","dimension","of","the","interaction","arch",".","Example",":",":","B","=","20","over_arch","=","OverArch","(",")","logits","=","over_arch","(","torch.rand","(","(","B",",","10",")",")",")"]
code: def __init__(self, in_features: int) -> None:
      super().__init__()
      self.model: nn.Module = nn.Sequential(
          nn.Linear(in_features, 1),
          nn.Sigmoid(),
      )
  
imports: from typing import List
  import torch
  from torch import nn
  from torchrec import EmbeddingBagCollection, KeyedJaggedTensor
  from torchrec.modules.deepfm import DeepFM, FactorizationMachine
  from torchrec.sparse.jagged_tensor import KeyedTensor
label_desc: Use image node_id 1 to create a new OverArch object from inherited base classes: nn with example: obj = OverArch(in_features)