class_docstr: Output only the context features.
  Parameters:
      in_features / out_features: the channels of the input / output feature maps.
      dropout: specify the dropout ratio
      fusion: We provide two different fusion method, "concat" or "add"
      size: we find that directly learn the attention weights on even 1/8 feature maps is hard.
  Return:
      features after "concat" or "add"
code: def forward(self, feats):
      priors = [stage(feats) for stage in self.stages]
      context = priors[0]
      for i in range(1, len(priors)):
          context += priors[i]
      output = self.conv_bn_dropout(context)
      return output
  
imports: import torch
  import torch.nn.functional
  from torch import nn
  from ..modules.activations import ABN
label_desc: Use image node_id 3 for calling the ObjectContextBlock obj's underlying member method code with example usage: obj.forward(feats) and returns: output