class_docstr: Processes the dense features of DLRM model.
  
  Args:
      in_features (int): dimensionality of the dense input features.
      layer_sizes (List[int]): list of layer sizes.
      device (Optional[torch.device]): default compute device.
  
  Example::
  
      B = 20
      D = 3
      dense_arch = DenseArch(10, layer_sizes=[15, D])
      dense_embedded = dense_arch(torch.rand((B, 10)))
class_docstr_tok: ["Processes","the","dense","features","of","DLRM","model",".","Args",":","in_features","(","int",")",":","dimensionality","of","the","dense","input","features",".","layer_sizes","(","List","[","int","]",")",":","list","of","layer","sizes",".","device","(","Optional","[","torch.device","]",")",":","default","compute","device",".","Example",":",":","B","=","20","D","=","3","dense_arch","=","DenseArch","(","10",",","layer_sizes=","[","15",",","D","]",")","dense_embedded","=","dense_arch","(","torch.rand","(","(","B",",","10",")",")",")"]
docstr: Args:
      features (torch.Tensor): an input tensor of dense features.
  
  Returns:
      torch.Tensor: an output tensor of size B X D.
docstr_tok: ["Args",":","features","(","torch.Tensor",")",":","an","input","tensor","of","dense","features",".","Returns",":","torch.Tensor",":","an","output","tensor","of","size","B","X","D","."]
code: def forward(self, features: torch.Tensor) -> torch.Tensor:
      """
      Args:
          features (torch.Tensor): an input tensor of dense features.
  
      Returns:
          torch.Tensor: an output tensor of size B X D.
      """
      return self.model(features)
  
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 2 for calling the DenseArch obj's underlying member method code with example usage: obj.forward(features) and returns: self