code: def __init__(
      self,
      num_branches,
      blocks,
      num_blocks,
      num_inchannels,
      num_channels,
      fuse_method,
      multi_scale_output=True,
      norm_layer=nn.BatchNorm2d,
      align_corners=True,
  ):
      super(HighResolutionModule, self).__init__()
      self._check_branches(
          num_branches, num_blocks, num_inchannels, num_channels
      )
  
      self.num_inchannels = num_inchannels
      self.fuse_method = fuse_method
      self.num_branches = num_branches
      self.norm_layer = norm_layer
      self.align_corners = align_corners
  
      self.multi_scale_output = multi_scale_output
  
      self.branches = self._make_branches(
          num_branches, blocks, num_blocks, num_channels
      )
      self.fuse_layers = self._make_fuse_layers()
      self.relu = nn.ReLU(inplace=relu_inplace)
  
imports: import os
  import torch
  import torch._utils
  import numpy
  import torch.nn
  import torch.nn.functional
  from cftool.misc import print_info
  from .ocr import SpatialOCR_Module
  from .ocr import SpatialGather_Module
  from .resnetv1b import BottleneckV1b
  from .resnetv1b import BasicBlockV1b
label_desc: Use image node_id 1 to create a new HighResolutionModule object from inherited base classes: nn with example: obj = HighResolutionModule(num_branches, blocks, num_blocks, num_inchannels, num_channels, fuse_method, multi_scale_output, norm_layer, align_corners)