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 _make_stage(
      self,
      in_channels,
      output_channels,
      key_channels,
      value_channels,
      size,
  ):
      return SelfAttentionBlock2D(
          in_channels,
          key_channels,
          value_channels,
          output_channels,
          size,
      )
  
imports: import torch
  import torch.nn.functional
  from torch import nn
  from ..modules.activations import ABN
label_desc: Use image node_id 2 for calling the ObjectContextBlock obj's underlying member method code with example usage: obj._make_stage(in_channels, output_channels, key_channels, value_channels, size) and returns: SelfAttentionBlock2D