class_docstr: AlexNetLegacy model as backbone
code: def __init__(self, width_mult=1, ctx=cpu(), **kwargs):
      configs = list(
          map(
              lambda x: 3 if x == 3 else int(x * width_mult),
              AlexNetLegacy.configs,
          )
      )
      super(AlexNetLegacy, self).__init__(**kwargs)
      with self.name_scope():
          self.features = nn.HybridSequential(prefix="")
          with self.features.name_scope():
              self.features.add(
                  nn.Conv2D(configs[1], kernel_size=11, strides=2),
                  nn.BatchNorm(),
                  nn.MaxPool2D(pool_size=3, strides=2),
                  nn.Activation("relu"),
              )
              self.features.add(
                  nn.Conv2D(configs[2], kernel_size=5),
                  nn.BatchNorm(),
                  nn.MaxPool2D(pool_size=3, strides=2),
                  nn.Activation("relu"),
              )
              self.features.add(
                  nn.Conv2D(configs[3], kernel_size=3),
                  nn.BatchNorm(),
                  nn.Activation("relu"),
              )
              self.features.add(
                  nn.Conv2D(configs[4], kernel_size=3),
                  nn.BatchNorm(),
                  nn.Activation("relu"),
              )
              self.features.add(
                  nn.Conv2D(configs[5], kernel_size=3), nn.BatchNorm()
              )
          self.features.initialize(ctx=ctx)
  
file_path: gluon-cv/gluoncv/model_zoo/siamrpn/siam_alexnet.py
imports: from __future__ import division
  from mxnet.gluon import nn
  from mxnet.gluon.block import HybridBlock
  from mxnet.context import cpu
label_desc: Use image node_id 1 to create a new AlexNetLegacy object from inherited base classes: HybridBlock with example: obj = AlexNetLegacy(width_mult, ctx)