code: def _build_network(self):
      self._backbone = NODEBackbone(self.hparams)
      # Embedding Layer
      self._embedding_layer = self._backbone._build_embedding_layer()
      # average first n channels of every tree, where n is the number of output targets for regression
      # and number of classes for classification
      # Not using config head because NODE has a specific head
      warnings.warn(
          "Ignoring head config because NODE has a specific head which subsets the tree outputs"
      )
      self._head = Lambda(self.subset)
  
imports: import warnings
  import torch
  import torch.nn
  from omegaconf import DictConfig
  from pytorch_tabular.models.common.layers import Embedding1dLayer
  from pytorch_tabular.utils import get_logger
  from ..base_model import BaseModel
  from ..common import activations
  from ..common.layers import Lambda
  from .architecture_blocks import DenseODSTBlock
label_desc: Use image node_id 7 for calling the NODEModel obj's underlying member method code with example usage: obj._build_network() without return types