code: def forward(self, input):
      assert len(input.shape) >= 2
      if len(input.shape) > 2:
          return self.forward(input.view(-1, input.shape[-1])).view(
              *input.shape[:-1], -1
          )
      # new input shape: [batch_size, in_features]
  
      feature_logits = self.feature_selection_logits
      feature_selectors = self.choice_function(feature_logits, dim=0)
      # ^--[in_features, num_trees, depth]
  
      feature_values = torch.einsum(
          "bi,ind->bnd", input, feature_selectors
      )
      # ^--[batch_size, num_trees, depth]
  
      threshold_logits = (
          feature_values - self.feature_thresholds
      ) * torch.exp(-self.log_temperatures)
  
      threshold_logits = torch.stack(
          [-threshold_logits, threshold_logits], dim=-1
      )
      # ^--[batch_size, num_trees, depth, 2]
  
      bins = self.bin_function(threshold_logits)
      # ^--[batch_size, num_trees, depth, 2], approximately binary
  
      bin_matches = torch.einsum(
          "btds,dcs->btdc", bins, self.bin_codes_1hot
      )
      # ^--[batch_size, num_trees, depth, 2 ** depth]
  
      response_weights = torch.prod(bin_matches, dim=-2)
      # ^-- [batch_size, num_trees, 2 ** depth]
  
      response = torch.einsum(
          "bnd,ncd->bnc", response_weights, self.response
      )
      # ^-- [batch_size, num_trees, tree_dim]
  
      return response.flatten(1, 2) if self.flatten_output else response
  
imports: import random
  from typing import Callable
  from warnings import warn
  import numpy
  import torch
  import torch.nn
  from pytorch_tabular.utils import check_numpy
  from ..layers import ModuleWithInit
  from .activations import RSoftmax, entmax15, sparsemax, sparsemoid
label_desc: Use image node_id 2 for calling the ODST obj's underlying member method code with example usage: obj.forward(input) and returns: unknown, self