code: def _build_network(self):
      # Deep Layers
      _curr_units = self.hparams.embedding_dim
      if self.hparams.deep_layers:
          # Linear Layers
          layers = []
          for units in self.hparams.layers.split("-"):
              layers.extend(
                  _linear_dropout_bn(
                      self.hparams.activation,
                      self.hparams.initialization,
                      self.hparams.use_batch_norm,
                      _curr_units,
                      int(units),
                      self.hparams.dropout,
                  )
              )
              _curr_units = int(units)
          self.linear_layers = nn.Sequential(*layers)
      # Projection to Multi-Headed Attention Dims
      self.attn_proj = nn.Linear(
          _curr_units, self.hparams.attn_embed_dim
      )
      _initialize_layers(
          self.hparams.activation,
          self.hparams.initialization,
          self.attn_proj,
      )
      # Multi-Headed Attention Layers
      self.self_attns = nn.ModuleList(
          [
              nn.MultiheadAttention(
                  self.hparams.attn_embed_dim,
                  self.hparams.num_heads,
                  dropout=self.hparams.attn_dropouts,
              )
              for _ in range(self.hparams.num_attn_blocks)
          ]
      )
      if self.hparams.has_residuals:
          self.V_res_embedding = torch.nn.Linear(
              _curr_units,
              self.hparams.attn_embed_dim * self.hparams.num_attn_blocks
              if self.hparams.attention_pooling
              else self.hparams.attn_embed_dim,
          )
      self.output_dim = (
          self.hparams.continuous_dim + self.hparams.categorical_dim
      ) * self.hparams.attn_embed_dim
      if self.hparams.attention_pooling:
          self.output_dim = (
              self.output_dim * self.hparams.num_attn_blocks
          )
  
file_path: pytorch_tabular/src/pytorch_tabular/models/autoint/autoint.py
imports: import torch
  import torch.nn
  from omegaconf import DictConfig
  from pytorch_tabular.models.common.layers import Embedding2dLayer
  from pytorch_tabular.utils import _initialize_layers, _linear_dropout_bn
  from ..base_model import BaseModel
label_desc: Use image node_id 2 for calling the AutoIntBackbone obj's underlying member method code with example usage: obj._build_network() without return types