code: def __init__(
      self,
      out_channels,
      in_channels,
      mid_channels=256,
      norm_layer=nn.BatchNorm2d,
  ):
      super(_DeepLabHead, self).__init__()
  
      self.block = nn.Sequential(
          SeparableConv2d(
              in_channels=in_channels,
              out_channels=mid_channels,
              dw_kernel=3,
              dw_padding=1,
              activation="relu",
              norm_layer=norm_layer,
          ),
          SeparableConv2d(
              in_channels=mid_channels,
              out_channels=mid_channels,
              dw_kernel=3,
              dw_padding=1,
              activation="relu",
              norm_layer=norm_layer,
          ),
          nn.Conv2d(
              in_channels=mid_channels,
              out_channels=out_channels,
              kernel_size=1,
          ),
      )
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from contextlib import ExitStack
  from .resnet import ResNetBackbone
  from .basic_blocks import select_activation_function
  from .basic_blocks import SeparableConv2d
label_desc: Use image node_id 1 to create a new _DeepLabHead object from inherited base classes: nn with example: obj = _DeepLabHead(out_channels, in_channels, mid_channels, norm_layer)