class_docstr: Global feature extractor module.
  This module consists of three InvertedBottleneck blocks (like inverted residual introduced by MobileNetV2) and
  a PPModule (introduced by PSPNet).
  Args:
      in_channels (int): The number of input channels to the module.
      block_channels (tuple): A tuple represents output channels of each bottleneck block.
      out_channels (int): The number of output channels of the module. Default:
      expansion (int): The expansion factor in bottleneck.
      num_blocks (tuple): It indicates the repeat time of each bottleneck.
      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_layer(
      self,
      block,
      in_channels,
      out_channels,
      blocks,
      expansion=6,
      stride=1,
  ):
      layers = []
      layers.append(block(in_channels, out_channels, expansion, stride))
      for _ in range(1, blocks):
          layers.append(block(out_channels, out_channels, expansion, 1))
      return nn.Sequential(*layers)
  
imports: import paddle.nn
  import paddle.nn.functional
  import paddle
  from paddleseg.cvlibs import manager
  from paddleseg.models import layers
  from paddleseg.utils import utils
label_desc: Use image node_id 2 for calling the GlobalFeatureExtractor obj's underlying member method code with example usage: obj._make_layer(block, in_channels, out_channels, blocks, expansion, stride) and returns: nn