class_docstr: Encoder Decoder segmentors.
  
  EncoderDecoder typically consists of backbone, decode_head, auxiliary_head.
  Note that auxiliary_head is only used for deep supervision during training,
  which could be dumped during inference.
class_docstr_tok: ["Encoder","Decoder","segmentors",".","EncoderDecoder","typically","consists","of","backbone",",","decode_head",",","auxiliary_head",".","Note","that","auxiliary_head","is","only","used","for","deep","supervision","during","training",",","which","could","be","dumped","during","inference","."]
docstr: Initialize ``auxiliary_head``
docstr_tok: ["Initialize","``","auxiliary_head","``"]
code: def _init_auxiliary_head(self, auxiliary_head):
      """Initialize ``auxiliary_head``"""
      if auxiliary_head is not None:
          if isinstance(auxiliary_head, list):
              self.auxiliary_head = nn.ModuleList()
              for head_cfg in auxiliary_head:
                  self.auxiliary_head.append(
                      builder.build_head(head_cfg)
                  )
          else:
              self.auxiliary_head = builder.build_head(auxiliary_head)
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from annotator.uniformer.mmseg.core import add_prefix
  from annotator.uniformer.mmseg.ops import resize
  from ..None import builder
  from ..builder import SEGMENTORS
  from .base import BaseSegmentor
label_desc: Use image node_id 3 for calling the EncoderDecoder obj's underlying member method code with example usage: obj._init_auxiliary_head(auxiliary_head) without return types