code: def forward(self, x):
      if self.num_branches == 1:
          return [self.branches[0](x[0])]
  
      for i in range(self.num_branches):
          x[i] = self.branches[i](x[i])
  
      x_fuse = []
      for i in range(len(self.fuse_layers)):
          y = x[0] if i == 0 else self.fuse_layers[i][0](x[0])
          for j in range(1, self.num_branches):
              if i == j:
                  y = y + x[j]
              elif j > i:
                  width_output = x[i].shape[-1]
                  height_output = x[i].shape[-2]
                  y = y + F.interpolate(
                      self.fuse_layers[i][j](x[j]),
                      size=[height_output, width_output],
                      mode="bilinear",
                      align_corners=ALIGN_CORNERS,
                  )
              else:
                  y = y + self.fuse_layers[i][j](x[j])
          x_fuse.append(self.relu(y))
  
      return x_fuse
  
imports: import os
  import logging
  import numpy
  import torch
  import torch.nn
  import torch._utils
  import torch.nn.functional
  from .bn_helper import BatchNorm2d, BatchNorm2d_class, relu_inplace
label_desc: Use image node_id 7 for calling the HighResolutionModule obj's underlying member method code with example usage: obj.forward(x) and returns: x_fuse, list