code: def forward(self, x):
      layer0 = self.layer0(x)
      x = self.layer1(layer0)
  
      x_list = []
      for i in range(self.stage2_cfg["NUM_BRANCHES"]):
          if self.transition1[i] is not None:
              x_list.append(self.transition1[i](x))
          else:
              x_list.append(x)
      y_list = self.stage2(x_list)
  
      x_list = []
      for i in range(self.stage3_cfg["NUM_BRANCHES"]):
          if self.transition2[i] is not None:
              x_list.append(self.transition2[i](y_list[-1]))
          else:
              x_list.append(y_list[i])
      y_list = self.stage3(x_list)
  
      x_list = []
      for i in range(self.stage4_cfg["NUM_BRANCHES"]):
          if self.transition3[i] is not None:
              x_list.append(self.transition3[i](y_list[-1]))
          else:
              x_list.append(y_list[i])
      y_list = self.stage4(x_list)
  
      outputs = _take([layer0] + y_list, self._layers)
      return outputs
  
imports: from collections import OrderedDict
  from typing import List
  import torch
  import torch.nn.functional
  from torch import nn
  from .common import EncoderModule, make_n_channel_input, _take
label_desc: Use image node_id 5 for calling the HRNetEncoderBase obj's underlying member method code with example usage: obj.forward(x) and returns: outputs