class_docstr: An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
  models.
code: def _init_weights(self, module):
      if isinstance(module, UperNetPreTrainedModel):
          module.backbone.init_weights()
          module.decode_head.init_weights()
          if module.auxiliary_head is not None:
              module.auxiliary_head.init_weights()
  
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 for calling the UperNetPreTrainedModel obj's underlying member method code with example usage: obj._init_weights(module) without return types