class_docstr: Aggregate the context features according to the initial predicted
  probability distribution.
  
  Employ the soft-weighted method to aggregate the context.
class_docstr_tok: ["Aggregate","the","context","features","according","to","the","initial","predicted","probability","distribution",".","Employ","the","soft-weighted","method","to","aggregate","the","context","."]
code: def __init__(self, scale):
      super(SpatialGatherModule, self).__init__()
      self.scale = scale
  
file_path: lavis/lavis/common/annotator/uniformer/mmseg/models/decode_heads/ocr_head.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from annotator.uniformer.mmcv.cnn import ConvModule
  from annotator.uniformer.mmseg.ops import resize
  from ..builder import HEADS
  from ..utils import SelfAttentionBlock
  from .cascade_decode_head import BaseCascadeDecodeHead
label_desc: Use image node_id 1 to create a new SpatialGatherModule object from inherited base classes: nn with example: obj = SpatialGatherModule(scale)