docstr: Performs data-aware initialization for NODE.
docstr_tok: ["Performs","data-aware","initialization","for","NODE","."]
code: def data_aware_initialization(self, datamodule):
      """Performs data-aware initialization for NODE."""
      logger.info(
          "Data Aware Initialization of NODE using a forward pass with "
          f"{self.hparams.data_aware_init_batch_size} batch size...."
      )
      # 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))
      for k, v in batch.items():
          if isinstance(v, list) and (len(v) == 0):
              # Skipping empty list
              continue
          # batch[k] = v.to("cpu" if self.config.gpu == 0 else "cuda")
          batch[k] = v.to(self.device)
  
      # single forward pass to initialize the ODST
      with torch.no_grad():
          self(batch)
  
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 3 for calling the NODEModel obj's underlying member method code with example usage: obj.data_aware_initialization(datamodule) without return types