class_docstr: High-Resolution Module for HRNet.
  
  In this module, every branch has 4 BasicBlocks/Bottlenecks. Fusion/Exchange
  is in this module.
class_docstr_tok: ["High-Resolution","Module","for","HRNet",".","In","this","module",",","every","branch","has","4","BasicBlocks\/Bottlenecks",".","Fusion\/Exchange","is","in","this","module","."]
code: def __init__(
      self,
      num_branches,
      blocks,
      num_blocks,
      in_channels,
      num_channels,
      multiscale_output=True,
      with_cp=False,
      conv_cfg=None,
      norm_cfg=dict(type="BN", requires_grad=True),
  ):
      super(HRModule, self).__init__()
      self._check_branches(
          num_branches, num_blocks, in_channels, num_channels
      )
  
      self.in_channels = in_channels
      self.num_branches = num_branches
  
      self.multiscale_output = multiscale_output
      self.norm_cfg = norm_cfg
      self.conv_cfg = conv_cfg
      self.with_cp = with_cp
      self.branches = self._make_branches(
          num_branches, blocks, num_blocks, num_channels
      )
      self.fuse_layers = self._make_fuse_layers()
      self.relu = nn.ReLU(inplace=False)
  
imports: import torch.nn
  from annotator.uniformer.mmcv.cnn import build_conv_layer, build_norm_layer, constant_init, kaiming_init
  from annotator.uniformer.mmcv.runner import load_checkpoint
  from annotator.uniformer.mmcv.utils.parrots_wrapper import _BatchNorm
  from annotator.uniformer.mmseg.ops import Upsample, resize
  from annotator.uniformer.mmseg.utils import get_root_logger
  from ..builder import BACKBONES
  from .resnet import BasicBlock, Bottleneck
label_desc: Use image node_id 1 to create a new HRModule object from inherited base classes: nn with example: obj = HRModule(num_branches, blocks, num_blocks, in_channels, num_channels, multiscale_output, with_cp, conv_cfg, norm_cfg)