code: def get_symbol(num_classes, alpha=1, resolution=224, **kwargs):
      assert (
          alpha in alpha_values
      ), "Invalid alpha=[{0}], must be one of [{1}]".format(
          alpha, alpha_values
      )
      assert resolution % 32 == 0, "resolution must be multpile of 32"
  
      base = int(32 * alpha)
  
      data = mx.symbol.Variable(name="data")  # 224
      depth = base  # 32*alpha
      conv_1 = Conv(
          data,
          num_filter=depth,
          kernel=(3, 3),
          pad=(1, 1),
          stride=(2, 2),
          name="conv_1",
      )  # 224/112
  
      depth = base  # 32*alpha
      conv_2_dw = Conv(
          conv_1,
          num_group=depth,
          num_filter=depth,
          kernel=(3, 3),
          pad=(1, 1),
          stride=(1, 1),
          name="conv_2_dw",
      )  # 112/112
      conv_2 = Conv(
          conv_2_dw,
          num_filter=depth * 2,
          kernel=(1, 1),
          pad=(0, 0),
          stride=(1, 1),
          name="conv_2",
      )  # 112/112
  
      depth = base * 2  # 64*alpha
      conv_3_dw = Conv(
          conv_2,
          num_group=depth,
          num_filter=depth,
          kernel=(3, 3),
          pad=(1, 1),
          stride=(2, 2),
          name="conv_3_dw",
      )  # 112/56
      conv_3 = Conv(
          conv_3_dw,
          num_filter=depth * 2,
          kernel=(1, 1),
          pad=(0, 0),
          stride=(1, 1),
          name="conv_3",
      )  # 56/56
  
      depth = base * 4  # 128*alpha
      conv_4_dw = Conv(
          conv_3,
          num_group=depth,
          num_filter=depth,
          kernel=(3, 3),
          pad=(1, 1),
          stride=(1, 1),
          name="conv_4_dw",
      )  # 56/56
      conv_4 = Conv(
          conv_4_dw,
          num_filter=depth,
          kernel=(1, 1),
          pad=(0, 0),
          stride=(1, 1),
          name="conv_4",
      )  # 56/56
  
      depth = base * 4  # 128*alpha
      conv_5_dw = Conv(
          conv_4,
          num_group=depth,
          num_filter=depth,
          kernel=(3, 3),
          pad=(1, 1),
          stride=(2, 2),
          name="conv_5_dw",
      )  # 56/28
      conv_5 = Conv(
          conv_5_dw,
          num_filter=depth * 2,
          kernel=(1, 1),
          pad=(0, 0),
          stride=(1, 1),
          name="conv_5",
      )  # 28/28
  
      depth = base * 8  # 256*alpha
      conv_6_dw = Conv(
          conv_5,
          num_group=depth,
          num_filter=depth,
          kernel=(3, 3),
          pad=(1, 1),
          stride=(1, 1),
          name="conv_6_dw",
      )  # 28/28
      conv_6 = Conv(
          conv_6_dw,
          num_filter=depth,
          kernel=(1, 1),
          pad=(0, 0),
          stride=(1, 1),
          name="conv_6",
      )  # 28/28
  
      depth = base * 8  # 256*alpha
      conv_7_dw = Conv(
          conv_6,
          num_group=depth,
          num_filter=depth,
          kernel=(3, 3),
          pad=(1, 1),
          stride=(2, 2),
          name="conv_7_dw",
      )  # 28/14
      conv_7 = Conv(
          conv_7_dw,
          num_filter=depth * 2,
          kernel=(1, 1),
          pad=(0, 0),
          stride=(1, 1),
          name="conv_7",
      )  # 14/14
  
      depth = base * 16  # 512*alpha
      conv_8_dw = Conv(
          conv_7,
          num_group=depth,
          num_filter=depth,
          kernel=(3, 3),
          pad=(1, 1),
          stride=(1, 1),
          name="conv_8_dw",
      )  # 14/14
      conv_8 = Conv(
          conv_8_dw,
          num_filter=depth,
          kernel=(1, 1),
          pad=(0, 0),
          stride=(1, 1),
          name="conv_8",
      )  # 14/14
      conv_9_dw = Conv(
          conv_8,
          num_group=depth,
          num_filter=depth,
          kernel=(3, 3),
          pad=(1, 1),
          stride=(1, 1),
          name="conv_9_dw",
      )  # 14/14
      conv_9 = Conv(
          conv_9_dw,
          num_filter=depth,
          kernel=(1, 1),
          pad=(0, 0),
          stride=(1, 1),
          name="conv_9",
      )  # 14/14
      conv_10_dw = Conv(
          conv_9,
          num_group=depth,
          num_filter=depth,
          kernel=(3, 3),
          pad=(1, 1),
          stride=(1, 1),
          name="conv_10_dw",
      )  # 14/14
      conv_10 = Conv(
          conv_10_dw,
          num_filter=depth,
          kernel=(1, 1),
          pad=(0, 0),
          stride=(1, 1),
          name="conv_10",
      )  # 14/14
      conv_11_dw = Conv(
          conv_10,
          num_group=depth,
          num_filter=depth,
          kernel=(3, 3),
          pad=(1, 1),
          stride=(1, 1),
          name="conv_11_dw",
      )  # 14/14
      conv_11 = Conv(
          conv_11_dw,
          num_filter=depth,
          kernel=(1, 1),
          pad=(0, 0),
          stride=(1, 1),
          name="conv_11",
      )  # 14/14
      conv_12_dw = Conv(
          conv_11,
          num_group=depth,
          num_filter=depth,
          kernel=(3, 3),
          pad=(1, 1),
          stride=(1, 1),
          name="conv_12_dw",
      )  # 14/14
      conv_12 = Conv(
          conv_12_dw,
          num_filter=depth,
          kernel=(1, 1),
          pad=(0, 0),
          stride=(1, 1),
          name="conv_12",
      )  # 14/14
  
      depth = base * 16  # 512*alpha
      conv_13_dw = Conv(
          conv_12,
          num_group=depth,
          num_filter=depth,
          kernel=(3, 3),
          pad=(1, 1),
          stride=(2, 2),
          name="conv_13_dw",
      )  # 14/7
      conv_13 = Conv(
          conv_13_dw,
          num_filter=depth * 2,
          kernel=(1, 1),
          pad=(0, 0),
          stride=(1, 1),
          name="conv_13",
      )  # 7/7
  
      depth = base * 32  # 1024*alpha
      conv_14_dw = Conv(
          conv_13,
          num_group=depth,
          num_filter=depth,
          kernel=(3, 3),
          pad=(1, 1),
          stride=(1, 1),
          name="conv_14_dw",
      )  # 7/7
      conv_14 = Conv(
          conv_14_dw,
          num_filter=depth,
          kernel=(1, 1),
          pad=(0, 0),
          stride=(1, 1),
          name="conv_14",
      )  # 7/7
  
      pool_size = int(resolution / 32)
      pool = mx.sym.Pooling(
          data=conv_14,
          kernel=(pool_size, pool_size),
          stride=(1, 1),
          pool_type="avg",
          name="global_pool",
      )
      flatten = mx.sym.Flatten(data=pool, name="flatten")
      fc = mx.symbol.FullyConnected(
          data=flatten, num_hidden=num_classes, name="fc"
      )
      softmax = mx.symbol.SoftmaxOutput(data=fc, name="softmax")
      return softmax
  
imports: import mxnet
label_desc: Use image node_id 4 for calling a global function with example usage: get_symbol(num_classes, alpha, resolution) and returns: softmax