code: def _make_one_branch(
      self,
      branch_index,
      block,
      num_blocks,
      num_channels,
      num_heads,
      num_window_sizes,
      num_mlp_ratios,
      drop_paths,
  ):
      layers = []
      layers.append(
          block(
              self.num_inchannels[branch_index],
              num_channels[branch_index],
              num_heads=num_heads[branch_index],
              window_size=num_window_sizes[branch_index],
              mlp_ratio=num_mlp_ratios[branch_index],
              drop_path=drop_paths[0],
          )
      )
  
      self.num_inchannels[branch_index] = (
          num_channels[branch_index] * block.expansion
      )
      for i in range(1, num_blocks[branch_index]):
          layers.append(
              block(
                  self.num_inchannels[branch_index],
                  num_channels[branch_index],
                  num_heads=num_heads[branch_index],
                  window_size=num_window_sizes[branch_index],
                  mlp_ratio=num_mlp_ratios[branch_index],
                  drop_path=drop_paths[i],
              )
          )
      return nn.Sequential(*layers)
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager, param_init
  from paddleseg.models import layers
  from paddleseg.utils import utils, logger
  from paddleseg.models.backbones.transformer_utils import *
label_desc: Use image node_id 3 for calling the HighResolutionTransformerModule obj's underlying member method code with example usage: obj._make_one_branch(branch_index, block, num_blocks, num_channels, num_heads, num_window_sizes, num_mlp_ratios, drop_paths) and returns: nn