class_docstr: Simple Context module.
  
  Args:
      in_channels (int): The number of input channels to pyramid pooling module.
      inter_channels (int): The number of inter channels to pyramid pooling module.
      out_channels (int): The number of output channels after pyramid pooling module.
      bin_sizes (tuple, optional): The out size of pooled feature maps. Default: (1, 3).
      align_corners (bool): An argument of F.interpolate. It should be set to False
          when the output size of feature is even, e.g. 1024x512, otherwise it is True, e.g. 769x769.
code: def _make_stage(self, in_channels, out_channels, size):
      prior = nn.AdaptiveAvgPool2D(output_size=size)
      conv = layers.ConvBNReLU(
          in_channels=in_channels,
          out_channels=out_channels,
          kernel_size=1,
      )
      return nn.Sequential(prior, conv)
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg import utils
  from paddleseg.models import layers
  from paddleseg.cvlibs import manager
  from paddleseg.utils import utils
label_desc: Use image node_id 2 for calling the PPContextModule obj's underlying member method code with example usage: obj._make_stage(in_channels, out_channels, size) and returns: nn