class_docstr: AuxiliaryNet to predict pose angles. 
docstr: Parameters
  ----------
  x : tensor
      input intermediate features
  
  Returns
  -------
  output: tensor
      the predicted pose angles
docstr_tok: ["Parameters","--","--","--","--","--","x",":","tensor","input","intermediate","features","Returns","--","--","--","-","output",":","tensor","the","predicted","pose","angles"]
code: def forward(self, x):
      """
      Parameters
      ----------
      x : tensor
          input intermediate features
  
      Returns
      -------
      output: tensor
          the predicted pose angles
      """
      x = self.conv1(x)
      x = self.conv2(x)
      x = self.conv3(x)
      x = self.conv4(x)
      x = self.max_pool1(x)
      x = x.view(x.size(0), -1)
      x = self.fc1(x)
      x = self.fc2(x)
  
      return x
  
file_path: nni/examples/nas/legacy/oneshot/pfld/lib/subnet.py
imports: from __future__ import absolute_import, division, print_function
  import torch
  import torch.nn
  from lib.ops import MBBlock, SeparableConv, SingleOperation, StemBlock, conv_bn
  from torch.nn import init
label_desc: Use image node_id 2 for calling the AuxiliaryNet obj's underlying member method code with example usage: obj.forward(x) and returns: x