class_docstr: Instance of the final searched architecture. Only used in re-training/inference stage.
class_docstr_tok: ["Instance","of","the","final","searched","architecture",".","Only","used","in","re-training\/inference","stage","."]
docstr: Args:
      x: input tensor.
docstr_tok: ["Args",":","x",":","input","tensor","."]
code: def forward(self, x: list[torch.Tensor]) -> list[torch.Tensor]:
      """
      Args:
          x: input tensor.
      """
      # generate path activation probability
      inputs = x
      for blk_idx in range(self.num_blocks):
          outputs = [
              torch.tensor(0.0, dtype=x[0].dtype, device=x[0].device)
          ] * self.num_depths
          for res_idx, activation in enumerate(
              self.arch_code_a[blk_idx].data
          ):
              if activation:
                  mod: CellInterface = self.cell_tree[
                      str((blk_idx, res_idx))
                  ]
                  _out = mod.forward(
                      x=inputs[self.arch_code2in[res_idx]], weight=None
                  )
                  outputs[self.arch_code2out[res_idx]] = (
                      outputs[self.arch_code2out[res_idx]] + _out
                  )
          inputs = outputs
  
      return inputs
  
imports: from __future__ import annotations
  import datetime
  import warnings
  from typing import Optional
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from monai.networks.blocks.dints_block import ActiConvNormBlock, FactorizedIncreaseBlock, FactorizedReduceBlock, P3DActiConvNormBlock
  from monai.networks.layers.factories import Conv
  from monai.networks.layers.utils import get_act_layer, get_norm_layer
  from monai.utils import optional_import
label_desc: Use image node_id 2 for calling the TopologyInstance obj's underlying member method code with example usage: obj.forward(x) and returns: inputs