code: def forward(self, oldx: torch.Tensor) -> torch.Tensor:
      # x has shape (..., input_width)
      x = oldx.reshape(-1, self.input_width)
      # x has shape (batch_size, input_width)
      batch_size = x.shape[0]
  
      logits = self.linear_in(
          x
      )  # (batch_size, parallel_size * n_nodes)
      logit_decisions = (
          logits > 0
      ).long()  # (batch_size, parallel_size * n_nodes)
      activations = self.activation(
          logits
      )  # (batch_size, parallel_size * n_nodes)
  
      # recursively descending by depth, enforce conditionality
      activations = activations.view(
          batch_size, self.parallel_size, self.n_nodes
      )  # (batch_size, parallel_size, n_nodes)
      decisions = logit_decisions.view(
          batch_size, self.parallel_size, self.n_nodes
      )  # (batch_size, parallel_size, n_nodes)
  
      with torch.no_grad():
          current_nodes = torch.zeros(
              (batch_size, self.parallel_size),
              dtype=torch.long,
              device=x.device,
          )
          decision_map = torch.zeros_like(
              decisions, dtype=torch.float
          )  # (batch_size, parallel_size, n_nodes)
          decision_map.scatter_(
              dim=2, index=current_nodes.unsqueeze(-1), value=1.0
          )
  
          for d in range(self.depth):
              current_platform = 2**d - 1
              next_platform = 2 ** (d + 1) - 1
              moves = torch.gather(
                  decisions, 2, current_nodes.unsqueeze(2)
              ).squeeze(2)
              next_nodes = (
                  (current_nodes - current_platform) * 2
                  + moves
                  + next_platform
              )
              decision_map.scatter_(2, next_nodes.unsqueeze(-1), 1.0)
              current_nodes = next_nodes
  
      activations = (
          activations * decision_map
      )  # (batch_size, parallel_size, n_nodes)
      new_logits = self.linear_out(
          activations.flatten(1, 2)
      )  # (batch_size, output_width)
  
      ret = new_logits.reshape_as(oldx)
      return ret
  
imports: import torch
  import torch.nn
  import math
label_desc: Use image node_id 2 for calling the FFF obj's underlying member method code with example usage: obj.forward(oldx) and returns: ret