code: def __init__(self, m=1.0, **kwargs):
      super(MNasNet, self).__init__(**kwargs)
  
      self.first_oup = int(32 * m)
      self.second_oup = int(16 * m)
      # self.second_oup = int(32*m)
      self.interverted_residual_setting = [
          # t, c,  n, s, k
          [3, int(24 * m), 3, 2, 3, "stage2_"],  # -> 56x56
          [3, int(40 * m), 3, 2, 5, "stage3_"],  # -> 28x28
          [6, int(80 * m), 3, 2, 5, "stage4_1_"],  # -> 14x14
          [6, int(96 * m), 2, 1, 3, "stage4_2_"],  # -> 14x14
          [6, int(192 * m), 4, 2, 5, "stage5_1_"],  # -> 7x7
          [6, int(320 * m), 1, 1, 3, "stage5_2_"],  # -> 7x7
      ]
      self.last_channels = int(1024 * m)
  
      with self.name_scope():
          self.features = nn.HybridSequential()
          self.features.add(
              ConvBlock(self.first_oup, 3, 1, prefix="stage1_conv0_")
          )
          self.features.add(
              SepCONV(
                  self.first_oup,
                  self.second_oup,
                  3,
                  prefix="stage1_sepconv0_",
              )
          )
          inp = self.second_oup
          for i, (t, c, n, s, k, prefix) in enumerate(
              self.interverted_residual_setting
          ):
              oup = c
              self.features.add(
                  ExpandedConvSequence(
                      t, k, inp, oup, n, s, prefix=prefix
                  )
              )
              inp = oup
  
          self.features.add(
              Conv1x1(self.last_channels, prefix="stage5_3_")
          )
  
imports: import sys
  import os
  import mxnet
  import mxnet.ndarray
  import mxnet.gluon
  import mxnet.gluon.nn
  import mxnet.autograd
  import symbol_utils
  from config import config
label_desc: Use image node_id 1 to create a new MNasNet object from inherited base classes: nn with example: obj = MNasNet(m)