class_docstr: Implementation of the BaseOC module
  Parameters:
      in_features / out_features: the channels of the input / output feature maps.
      dropout: we choose 0.05 as the default value.
      size: you can apply multiple sizes. Here we only use one size.
  Return:
      features fused with Object context information.
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 BaseOC_Module obj's underlying member method code with example usage: obj._make_stage(in_channels, output_channels, key_channels, value_channels, size) and returns: SelfAttentionBlock2D