class_docstr: Global feature extractor module
class_docstr_tok: ["Global","feature","extractor","module"]
code: def __init__(
      self,
      in_channels=64,
      block_channels=(64, 96, 128),
      out_channels=128,
      t=6,
      num_blocks=(3, 3, 3),
      height=32,
      width=64,
      norm_layer=nn.BatchNorm,
      norm_kwargs=None,
      **kwargs,
  ):
      super(GlobalFeatureExtractor, self).__init__()
      self.num_blocks = num_blocks
      with self.name_scope():
          self.bottleneck1 = self._make_layer(
              LinearBottleneck,
              in_channels,
              block_channels[0],
              num_blocks[0],
              t,
              2,
              norm_layer,
              norm_kwargs,
          )
          self.bottleneck2 = self._make_layer(
              LinearBottleneck,
              block_channels[0],
              block_channels[1],
              num_blocks[1],
              t,
              2,
              norm_layer,
              norm_kwargs,
          )
          self.bottleneck3 = self._make_layer(
              LinearBottleneck,
              block_channels[1],
              block_channels[2],
              num_blocks[2],
              t,
              1,
              norm_layer,
              norm_kwargs,
          )
          self.ppm = _FastPyramidPooling(
              block_channels[2],
              out_channels,
              height=height,
              width=width,
              norm_layer=norm_layer,
              norm_kwargs=norm_kwargs,
          )
  
imports: from mxnet.context import cpu
  from mxnet.gluon.block import HybridBlock
  from mxnet.gluon import nn
label_desc: Use image node_id 1 to create a new GlobalFeatureExtractor object from inherited base classes: HybridBlock with example: obj = GlobalFeatureExtractor(in_channels, block_channels, out_channels, t, num_blocks, height, width, norm_layer, norm_kwargs)