code: def __init__(
      self,
      cat_embedding_dims: list,
      n_continuous_features: int,
      gflu_stages: int,
      gflu_dropout: float = 0.0,
      gflu_feature_init_sparsity: float = 0.3,
      learnable_sparsity: bool = True,
      batch_norm_continuous_input: bool = True,
      virtual_batch_size: int = None,
      embedding_dropout: float = 0.0,
  ):
      super().__init__()
      self.gflu_stages = gflu_stages
      self.gflu_dropout = gflu_dropout
      self.batch_norm_continuous_input = batch_norm_continuous_input
      self.n_continuous_features = n_continuous_features
      self.cat_embedding_dims = cat_embedding_dims
      self._embedded_cat_features = sum(
          [y for x, y in cat_embedding_dims]
      )
      self.n_features = (
          self._embedded_cat_features + n_continuous_features
      )
      self.embedding_dropout = embedding_dropout
      self.output_dim = (
          self.n_continuous_features + self._embedded_cat_features
      )
      self.gflu_feature_init_sparsity = gflu_feature_init_sparsity
      self.learnable_sparsity = learnable_sparsity
      self.virtual_batch_size = virtual_batch_size
      self._build_network()
  
imports: import torch
  import torch.nn
  from omegaconf import DictConfig
  from pytorch_tabular.models.common.layers import Add, Embedding1dLayer, GatedFeatureLearningUnit
  from pytorch_tabular.models.common.layers.activations import t_softmax
  from pytorch_tabular.utils import get_logger
  from ..base_model import BaseModel
label_desc: Use image node_id 1 to create a new GANDALFBackbone object from inherited base classes: nn with example: obj = GANDALFBackbone(cat_embedding_dims, n_continuous_features, gflu_stages, gflu_dropout, gflu_feature_init_sparsity, learnable_sparsity, batch_norm_continuous_input, virtual_batch_size, embedding_dropout)