code: def __init__(
      self,
      input_dim,
      num_trees,
      num_layers,
      tree_output_dim=1,
      max_features=None,
      input_dropout=0.0,
      flatten_output=False,
      Module=ODST,
      **kwargs,
  ):
      layers = []
      for i in range(num_layers):
          oddt = Module(
              input_dim,
              num_trees,
              tree_output_dim=tree_output_dim,
              flatten_output=True,
              **kwargs,
          )
          input_dim = min(
              input_dim + num_trees * tree_output_dim,
              max_features or float("inf"),
          )
          layers.append(oddt)
  
      super().__init__(*layers)
      self.num_layers, self.layer_dim, self.tree_dim = (
          num_layers,
          num_trees,
          tree_output_dim,
      )
      self.max_features, self.flatten_output = (
          max_features,
          flatten_output,
      )
      self.input_dropout = input_dropout
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from pytorch_tabular.models.common.layers import ODST
label_desc: Use image node_id 1 to create a new DenseODSTBlock object from inherited base classes: nn with example: obj = DenseODSTBlock(input_dim, num_trees, num_layers, tree_output_dim, max_features, input_dropout, flatten_output, Module)