class_docstr: interp_type can be 'nearest'/'bilinear'/'bilinear_like'
class_docstr_tok: ["interp_type","can","be","'nearest'\/'bilinear'\/'bilinear_like","'"]
code: def __init__(
      self,
      num_branches,
      blocks,
      num_blocks,
      num_channels,
      fuse_method,
      num_inchannels=None,
      multi_scale_output=True,
      interp_type="nearest",
      norm_layer=BatchNorm,
      norm_kwargs=None,
      **kwargs,
  ):
      super(HighResolutionModule, self).__init__(**kwargs)
  
      if num_inchannels is not None:
          self.num_inchannels = num_inchannels
      else:
          self.num_inchannels = num_channels
  
      self.fuse_method = fuse_method
      self.num_branches = num_branches
  
      self.multi_scale_output = multi_scale_output
      self.interp_type = interp_type
  
      self.branches = self._make_branches(
          num_branches, blocks, num_blocks, num_channels
      )
      self.fuse_layers = self._make_fuse_layers(
          norm_layer=norm_layer, norm_kwargs=norm_kwargs
      )
  
imports: import numpy
  from mxnet.gluon import contrib
  from mxnet.gluon import nn
  from mxnet.gluon.nn import BatchNorm
  from mxnet.context import cpu
  from .resnet import BasicBlockV1, BottleneckV1, _conv3x3
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_channels, fuse_method, num_inchannels, multi_scale_output, interp_type, norm_layer, norm_kwargs)