class_docstr: Just a batchnorm.
class_docstr_tok: ["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
          )
  
file_path: dinov2/dinov2/eval/depth/models/decode_heads/linear_head.py
imports: import torch
  import torch.nn
  from ...ops import resize
  from ..builder import HEADS
  from .decode_head import DepthBaseDecodeHead
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)