code: def _build_network(self):
      # Linear Layers
      layers = []
      if hasattr(self.hparams, "_backbone_input_dim"):
          _curr_units = (
              self.hparams._backbone_input_dim
          )  # TODO implement this backdoor in every model?
      else:
          _curr_units = (
              self.hparams.embedded_cat_dim
              + self.hparams.continuous_dim
          )
      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)
      _initialize_layers(
          self.hparams.activation,
          self.hparams.initialization,
          self.linear_layers,
      )
      self.output_dim = _curr_units
  
file_path: pytorch_tabular/src/pytorch_tabular/models/category_embedding/category_embedding_model.py
imports: import torch
  import torch.nn
  from omegaconf import DictConfig
  from pytorch_tabular.models.common.layers import Embedding1dLayer
  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 CategoryEmbeddingBackbone obj's underlying member method code with example usage: obj._build_network() without return types