class_docstr: Processes the dense features of DeepFMNN model. Output layer is sized to
  the embedding_dimension of the EmbeddingBagCollection embeddings.
  
  Args:
      in_features (int): dimensionality of the dense input features.
      hidden_layer_size (int): sizes of the hidden layers in the DenseArch.
      embedding_dim (int): the same size of the embedding_dimension of sparseArch.
      device (torch.device): default compute device.
  
  Example::
  
      B = 20
      D = 3
      in_features = 10
      dense_arch = DenseArch(
          in_features=in_features, hidden_layer_size=10, embedding_dim=D
      )
  
      dense_arch_input = torch.rand((B, in_features))
      dense_embedded = dense_arch(dense_arch_input)
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code: def __init__(
      self,
      in_features: int,
      hidden_layer_size: int,
      embedding_dim: int,
  ) -> None:
      super().__init__()
      self.model: nn.Module = nn.Sequential(
          nn.Linear(in_features, hidden_layer_size),
          nn.ReLU(),
          nn.Linear(hidden_layer_size, embedding_dim),
          nn.ReLU(),
      )
  
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 DenseArch object from inherited base classes: nn with example: obj = DenseArch(in_features, hidden_layer_size, embedding_dim)