code: def __init__(self, config):
      super().__init__(config)
  
      self.backbone = AutoBackbone.from_config(config.backbone_config)
  
      # Semantic segmentation head(s)
      self.decode_head = UperNetHead(
          config, in_channels=self.backbone.channels
      )
      self.auxiliary_head = (
          UperNetFCNHead(config) if config.use_auxiliary_head else None
      )
  
      # Initialize weights and apply final processing
      self.post_init()
  
imports: from typing import List, Optional, Tuple, Union
  import torch
  from torch import nn
  from torch.nn import CrossEntropyLoss
  from ...None import AutoBackbone
  from ...modeling_outputs import SemanticSegmenterOutput
  from ...modeling_utils import PreTrainedModel
  from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
  from .configuration_upernet import UperNetConfig
label_desc: Use image node_id 1 to create a new UperNetForSemanticSegmentation object from inherited base classes: UperNetPreTrainedModel with example: obj = UperNetForSemanticSegmentation(config)