class_docstr: Final Arch of DLRM - simple MLP over OverArch.
  
  Args:
      in_features (int): size of the input.
      layer_sizes (List[int]): sizes of the layers of the `OverArch`.
      device (Optional[torch.device]): default compute device.
  
  Example::
  
      B = 20
      D = 3
      over_arch = OverArch(10, [5, 1])
      logits = over_arch(torch.rand((B, 10)))
class_docstr_tok: ["Final","Arch","of","DLRM","-","simple","MLP","over","OverArch",".","Args",":","in_features","(","int",")",":","size","of","the","input",".","layer_sizes","(","List","[","int","]",")",":","sizes","of","the","layers","of","the","`","OverArch","`",".","device","(","Optional","[","torch.device","]",")",":","default","compute","device",".","Example",":",":","B","=","20","D","=","3","over_arch","=","OverArch","(","10",",","[","5",",","1","]",")","logits","=","over_arch","(","torch.rand","(","(","B",",","10",")",")",")"]
code: def __init__(
      self,
      in_features: int,
      layer_sizes: List[int],
      device: Optional[torch.device] = None,
  ) -> None:
      super().__init__()
      if len(layer_sizes) <= 1:
          raise ValueError("OverArch must have multiple layers.")
      self.model: nn.Module = nn.Sequential(
          MLP(
              in_features,
              layer_sizes[:-1],
              bias=True,
              activation="relu",
              device=device,
          ),
          nn.Linear(
              layer_sizes[-2], layer_sizes[-1], bias=True, device=device
          ),
      )
  
imports: from typing import Dict, List, Optional, Tuple
  import torch
  from torch import nn
  from torchrec.datasets.utils import Batch
  from torchrec.modules.crossnet import LowRankCrossNet
  from torchrec.modules.embedding_modules import EmbeddingBagCollection
  from torchrec.modules.mlp import MLP
  from torchrec.sparse.jagged_tensor import KeyedJaggedTensor, 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, layer_sizes, device)