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",")",")",")"]
docstr: Args:
      features (torch.Tensor):
  
  Returns:
      torch.Tensor: an output tensor of size B X 1.
docstr_tok: ["Args",":","features","(","torch.Tensor",")",":","Returns",":","torch.Tensor",":","an","output","tensor","of","size","B","X","1","."]
code: def forward(self, features: torch.Tensor) -> torch.Tensor:
      """
      Args:
          features (torch.Tensor):
  
      Returns:
          torch.Tensor: an output tensor of size B X 1.
      """
      return self.model(features)
  
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 2 for calling the OverArch obj's underlying member method code with example usage: obj.forward(features) and returns: self