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: Encode images with backbone and decode into a semantic segmentation
  map of the same size as input.
docstr_tok: ["Encode","images","with","backbone","and","decode","into","a","semantic","segmentation","map","of","the","same","size","as","input","."]
code: def encode_decode(self, img, img_metas):
      """Encode images with backbone and decode into a semantic segmentation
      map of the same size as input."""
      x = self.extract_feat(img)
      out = self._decode_head_forward_test(x, img_metas)
      out = resize(
          input=out,
          size=img.shape[2:],
          mode="bilinear",
          align_corners=self.align_corners,
      )
      return out
  
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 6 for calling the EncoderDecoder obj's underlying member method code with example usage: obj.encode_decode(img, img_metas) and returns: out