code: def get_symbol(num_classes=1000, version="1.0", **kwargs):
      """Get symbol of SqueezeNet
  
      Parameters
      ----------
      num_classes: int
          The number of classification results
  
      version : str, optional
          "1.0" or "1.1" of SqueezeNet
      """
      assert version in [
          "1.0",
          "1.1",
      ], (
          "Unsupported SqueezeNet version {version}:"
          "1.0 or 1.1 expected".format(version=version)
      )
      net = mx.sym.Variable("data")
      if version == "1.0":
          net = mx.sym.Convolution(
              net,
              num_filter=96,
              kernel=(7, 7),
              stride=(2, 2),
              pad=(3, 3),
          )
          net = mx.sym.Activation(net, act_type="relu")
          net = mx.sym.Pooling(
              data=net, kernel=(3, 3), pool_type="max", stride=(2, 2)
          )
          net = _make_fire(net, 16, 64, 64)
          net = _make_fire(net, 16, 64, 64)
          net = _make_fire(net, 32, 128, 128)
          net = mx.sym.Pooling(
              data=net, kernel=(3, 3), pool_type="max", stride=(2, 2)
          )
          net = _make_fire(net, 32, 128, 128)
          net = _make_fire(net, 48, 192, 192)
          net = _make_fire(net, 48, 192, 192)
          net = _make_fire(net, 64, 256, 256)
          net = mx.sym.Pooling(
              data=net, kernel=(3, 3), pool_type="max", stride=(2, 2)
          )
          net = _make_fire(net, 64, 256, 256)
      else:
          net = mx.sym.Convolution(
              net,
              num_filter=64,
              kernel=(3, 3),
              stride=(2, 2),
              pad=(1, 1),
          )
          net = mx.sym.Activation(net, act_type="relu")
          net = mx.sym.Pooling(
              data=net, kernel=(3, 3), pool_type="max", stride=(2, 2)
          )
          net = _make_fire(net, 16, 64, 64)
          net = _make_fire(net, 16, 64, 64)
          net = mx.sym.Pooling(
              data=net, kernel=(3, 3), pool_type="max", stride=(2, 2)
          )
          net = _make_fire(net, 32, 128, 128)
          net = _make_fire(net, 32, 128, 128)
          net = mx.sym.Pooling(
              data=net, kernel=(3, 3), pool_type="max", stride=(2, 2)
          )
          net = _make_fire(net, 48, 192, 192)
          net = _make_fire(net, 48, 192, 192)
          net = _make_fire(net, 64, 256, 256)
          net = _make_fire(net, 64, 256, 256)
      net = mx.sym.Dropout(net, p=0.5)
      net = mx.sym.Convolution(
          net, num_filter=num_classes, kernel=(1, 1)
      )
      net = mx.sym.Activation(net, act_type="relu")
      net = mx.sym.Pooling(
          data=net, global_pool=True, kernel=(13, 13), pool_type="avg"
      )
      net = mx.sym.flatten(net)
      return mx.sym.softmax(net)
  
imports: import mxnet
label_desc: Use image node_id 3 for calling a global function with example usage: get_symbol(num_classes, version) and returns: mx