code: def forward(self, x):
      initial_features = x.shape[-1]
      for layer in self:
          layer_inp = x
          if self.max_features is not None:
              tail_features = (
                  min(self.max_features, layer_inp.shape[-1])
                  - initial_features
              )
              if tail_features != 0:
                  layer_inp = torch.cat(
                      [
                          layer_inp[..., :initial_features],
                          layer_inp[..., -tail_features:],
                      ],
                      dim=-1,
                  )
          if self.training and self.input_dropout:
              layer_inp = F.dropout(layer_inp, self.input_dropout)
          h = layer(layer_inp)
          x = torch.cat([x, h], dim=-1)
  
      outputs = x[..., initial_features:]
      if not self.flatten_output:
          outputs = outputs.view(
              *outputs.shape[:-1],
              self.num_layers * self.layer_dim,
              self.tree_dim,
          )
      return outputs
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from pytorch_tabular.models.common.layers import ODST
label_desc: Use image node_id 2 for calling the DenseODSTBlock obj's underlying member method code with example usage: obj.forward(x) and returns: outputs