code: def data_aware_initialization(self, datamodule):
      if self.hparams.task == "regression":
          logger.info("Data Aware Initialization of T0")
          # Need a big batch to initialize properly
          alt_loader = datamodule.train_dataloader(
              batch_size=self.hparams.data_aware_init_batch_size
          )
          batch = next(iter(alt_loader))
          t0 = torch.mean(batch["target"], dim=0)
          if self.hparams.num_trees != 0:
              self.head.T0.data = t0
          else:
              self.T0.data = t0
  
imports: import torch
  import torch.nn
  from omegaconf import DictConfig
  from pytorch_tabular.models.common.heads import blocks
  from pytorch_tabular.models.common.layers import Add, Embedding1dLayer, GatedFeatureLearningUnit, NeuralDecisionTree
  from pytorch_tabular.models.common.layers.activations import entmax15, entmoid15, sparsemax, sparsemoid, t_softmax
  from pytorch_tabular.utils import get_logger
  from ..base_model import BaseModel
label_desc: Use image node_id 6 for calling the GatedAdditiveTreeEnsembleModel obj's underlying member method code with example usage: obj.data_aware_initialization(datamodule) without return types