class_docstr: Just a batchnorm.
code: def __init__(
      self,
      input_transform="resize_concat",
      in_index=(0, 1, 2, 3),
      upsample=1,
      **kwargs,
  ):
      super().__init__(**kwargs)
      self.input_transform = input_transform
      self.in_index = in_index
      self.upsample = upsample
      # self.bn = nn.SyncBatchNorm(self.in_channels)
      if self.classify:
          self.conv_depth = nn.Conv2d(
              self.channels,
              self.n_bins,
              kernel_size=1,
              padding=0,
              stride=1,
          )
      else:
          self.conv_depth = nn.Conv2d(
              self.channels, 1, kernel_size=1, padding=0, stride=1
          )
  
imports: import copy
  from functools import partial
  import math
  import warnings
  import torch
  import torch.nn
  from .ops import resize
label_desc: Use image node_id 1 to create a new BNHead object from inherited base classes: DepthBaseDecodeHead with example: obj = BNHead(input_transform, in_index, upsample)