code: def KitModel(
      weight_file=None,
      data=tf.placeholder(
          tf.float32, shape=(None, 112, 112, 3), name="data"
      ),
  ):
      global __weights_dict
      __weights_dict = load_weights(weight_file)
  
      minusscalar0_second = tf.constant(
          __weights_dict["minusscalar0_second"]["value"],
          name="minusscalar0_second",
      )
  
      mulscalar0_second = tf.constant(
          __weights_dict["mulscalar0_second"]["value"],
          name="mulscalar0_second",
      )
      minusscalar0 = data - minusscalar0_second
      mulscalar0 = minusscalar0 * mulscalar0_second
      conv0_pad = tf.pad(
          mulscalar0, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]
      )
      conv0 = convolution(
          conv0_pad,
          group=1,
          strides=[1, 1],
          padding="VALID",
          name="conv0",
      )
      bn0 = batch_normalization(
          conv0, variance_epsilon=1.9999999494757503e-05, name="bn0"
      )
      relu0 = prelu(bn0, name="relu0")
      stage1_unit1_bn1 = batch_normalization(
          relu0,
          variance_epsilon=1.9999999494757503e-05,
          name="stage1_unit1_bn1",
      )
      stage1_unit1_conv1sc = convolution(
          relu0,
          group=1,
          strides=[2, 2],
          padding="VALID",
          name="stage1_unit1_conv1sc",
      )
      stage1_unit1_conv1_pad = tf.pad(
          stage1_unit1_bn1, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]
      )
      stage1_unit1_conv1 = convolution(
          stage1_unit1_conv1_pad,
          group=1,
          strides=[1, 1],
          padding="VALID",
          name="stage1_unit1_conv1",
      )
      stage1_unit1_sc = batch_normalization(
          stage1_unit1_conv1sc,
          variance_epsilon=1.9999999494757503e-05,
          name="stage1_unit1_sc",
      )
      stage1_unit1_bn2 = batch_normalization(
          stage1_unit1_conv1,
          variance_epsilon=1.9999999494757503e-05,
          name="stage1_unit1_bn2",
      )
      stage1_unit1_relu1 = prelu(
          stage1_unit1_bn2, name="stage1_unit1_relu1"
      )
      stage1_unit1_conv2_pad = tf.pad(
          stage1_unit1_relu1, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]
      )
      stage1_unit1_conv2 = convolution(
          stage1_unit1_conv2_pad,
          group=1,
          strides=[2, 2],
          padding="VALID",
          name="stage1_unit1_conv2",
      )
      stage1_unit1_bn3 = batch_normalization(
          stage1_unit1_conv2,
          variance_epsilon=1.9999999494757503e-05,
          name="stage1_unit1_bn3",
      )
      plus0 = stage1_unit1_bn3 + stage1_unit1_sc
      stage1_unit2_bn1 = batch_normalization(
          plus0,
          variance_epsilon=1.9999999494757503e-05,
          name="stage1_unit2_bn1",
      )
      stage1_unit2_conv1_pad = tf.pad(
          stage1_unit2_bn1, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]
      )
      stage1_unit2_conv1 = convolution(
          stage1_unit2_conv1_pad,
          group=1,
          strides=[1, 1],
          padding="VALID",
          name="stage1_unit2_conv1",
      )
      stage1_unit2_bn2 = batch_normalization(
          stage1_unit2_conv1,
          variance_epsilon=1.9999999494757503e-05,
          name="stage1_unit2_bn2",
      )
      stage1_unit2_relu1 = prelu(
          stage1_unit2_bn2, name="stage1_unit2_relu1"
      )
      stage1_unit2_conv2_pad = tf.pad(
          stage1_unit2_relu1, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]
      )
      stage1_unit2_conv2 = convolution(
          stage1_unit2_conv2_pad,
          group=1,
          strides=[1, 1],
          padding="VALID",
          name="stage1_unit2_conv2",
      )
      stage1_unit2_bn3 = batch_normalization(
          stage1_unit2_conv2,
          variance_epsilon=1.9999999494757503e-05,
          name="stage1_unit2_bn3",
      )
      plus1 = stage1_unit2_bn3 + plus0
      stage1_unit3_bn1 = batch_normalization(
          plus1,
          variance_epsilon=1.9999999494757503e-05,
          name="stage1_unit3_bn1",
      )
      stage1_unit3_conv1_pad = tf.pad(
          stage1_unit3_bn1, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]
      )
      stage1_unit3_conv1 = convolution(
          stage1_unit3_conv1_pad,
          group=1,
          strides=[1, 1],
          padding="VALID",
          name="stage1_unit3_conv1",
      )
      stage1_unit3_bn2 = batch_normalization(
          stage1_unit3_conv1,
          variance_epsilon=1.9999999494757503e-05,
          name="stage1_unit3_bn2",
      )
      stage1_unit3_relu1 = prelu(
          stage1_unit3_bn2, name="stage1_unit3_relu1"
      )
      stage1_unit3_conv2_pad = tf.pad(
          stage1_unit3_relu1, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]
      )
      stage1_unit3_conv2 = convolution(
          stage1_unit3_conv2_pad,
          group=1,
          strides=[1, 1],
          padding="VALID",
          name="stage1_unit3_conv2",
      )
      stage1_unit3_bn3 = batch_normalization(
          stage1_unit3_conv2,
          variance_epsilon=1.9999999494757503e-05,
          name="stage1_unit3_bn3",
      )
      plus2 = stage1_unit3_bn3 + plus1
      stage2_unit1_bn1 = batch_normalization(
          plus2,
          variance_epsilon=1.9999999494757503e-05,
          name="stage2_unit1_bn1",
      )
      stage2_unit1_conv1sc = convolution(
          plus2,
          group=1,
          strides=[2, 2],
          padding="VALID",
          name="stage2_unit1_conv1sc",
      )
      stage2_unit1_conv1_pad = tf.pad(
          stage2_unit1_bn1, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]
      )
      stage2_unit1_conv1 = convolution(
          stage2_unit1_conv1_pad,
          group=1,
          strides=[1, 1],
          padding="VALID",
          name="stage2_unit1_conv1",
      )
      stage2_unit1_sc = batch_normalization(
          stage2_unit1_conv1sc,
          variance_epsilon=1.9999999494757503e-05,
          name="stage2_unit1_sc",
      )
      stage2_unit1_bn2 = batch_normalization(
          stage2_unit1_conv1,
          variance_epsilon=1.9999999494757503e-05,
          name="stage2_unit1_bn2",
      )
      stage2_unit1_relu1 = prelu(
          stage2_unit1_bn2, name="stage2_unit1_relu1"
      )
      stage2_unit1_conv2_pad = tf.pad(
          stage2_unit1_relu1, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]
      )
      stage2_unit1_conv2 = convolution(
          stage2_unit1_conv2_pad,
          group=1,
          strides=[2, 2],
          padding="VALID",
          name="stage2_unit1_conv2",
      )
      stage2_unit1_bn3 = batch_normalization(
          stage2_unit1_conv2,
          variance_epsilon=1.9999999494757503e-05,
          name="stage2_unit1_bn3",
      )
      plus3 = stage2_unit1_bn3 + stage2_unit1_sc
      stage2_unit2_bn1 = batch_normalization(
          plus3,
          variance_epsilon=1.9999999494757503e-05,
          name="stage2_unit2_bn1",
      )
      stage2_unit2_conv1_pad = tf.pad(
          stage2_unit2_bn1, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]
      )
      stage2_unit2_conv1 = convolution(
          stage2_unit2_conv1_pad,
          group=1,
          strides=[1, 1],
          padding="VALID",
          name="stage2_unit2_conv1",
      )
      stage2_unit2_bn2 = batch_normalization(
          stage2_unit2_conv1,
          variance_epsilon=1.9999999494757503e-05,
          name="stage2_unit2_bn2",
      )
      stage2_unit2_relu1 = prelu(
          stage2_unit2_bn2, name="stage2_unit2_relu1"
      )
      stage2_unit2_conv2_pad = tf.pad(
          stage2_unit2_relu1, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]
      )
      stage2_unit2_conv2 = convolution(
          stage2_unit2_conv2_pad,
          group=1,
          strides=[1, 1],
          padding="VALID",
          name="stage2_unit2_conv2",
      )
      stage2_unit2_bn3 = batch_normalization(
          stage2_unit2_conv2,
          variance_epsilon=1.9999999494757503e-05,
          name="stage2_unit2_bn3",
      )
      plus4 = stage2_unit2_bn3 + plus3
      stage2_unit3_bn1 = batch_normalization(
          plus4,
          variance_epsilon=1.9999999494757503e-05,
          name="stage2_unit3_bn1",
      )
      stage2_unit3_conv1_pad = tf.pad(
          stage2_unit3_bn1, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]
      )
      stage2_unit3_conv1 = convolution(
          stage2_unit3_conv1_pad,
          group=1,
          strides=[1, 1],
          padding="VALID",
          name="stage2_unit3_conv1",
      )
      stage2_unit3_bn2 = batch_normalization(
          stage2_unit3_conv1,
          variance_epsilon=1.9999999494757503e-05,
          name="stage2_unit3_bn2",
      )
      stage2_unit3_relu1 = prelu(
          stage2_unit3_bn2, name="stage2_unit3_relu1"
      )
      stage2_unit3_conv2_pad = tf.pad(
          stage2_unit3_relu1, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]
      )
      stage2_unit3_conv2 = convolution(
          stage2_unit3_conv2_pad,
          group=1,
          strides=[1, 1],
          padding="VALID",
          name="stage2_unit3_conv2",
      )
      stage2_unit3_bn3 = batch_normalization(
          stage2_unit3_conv2,
          variance_epsilon=1.9999999494757503e-05,
          name="stage2_unit3_bn3",
      )
      plus5 = stage2_unit3_bn3 + plus4
      stage2_unit4_bn1 = batch_normalization(
          plus5,
          variance_epsilon=1.9999999494757503e-05,
          name="stage2_unit4_bn1",
      )
      stage2_unit4_conv1_pad = tf.pad(
          stage2_unit4_bn1, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]
      )
      stage2_unit4_conv1 = convolution(
          stage2_unit4_conv1_pad,
          group=1,
          strides=[1, 1],
          padding="VALID",
          name="stage2_unit4_conv1",
      )
      stage2_unit4_bn2 = batch_normalization(
          stage2_unit4_conv1,
          variance_epsilon=1.9999999494757503e-05,
          name="stage2_unit4_bn2",
      )
      stage2_unit4_relu1 = prelu(
          stage2_unit4_bn2, name="stage2_unit4_relu1"
      )
      stage2_unit4_conv2_pad = tf.pad(
          stage2_unit4_relu1, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]
      )
      stage2_unit4_conv2 = convolution(
          stage2_unit4_conv2_pad,
          group=1,
          strides=[1, 1],
          padding="VALID",
          name="stage2_unit4_conv2",
      )
      stage2_unit4_bn3 = batch_normalization(
          stage2_unit4_conv2,
          variance_epsilon=1.9999999494757503e-05,
          name="stage2_unit4_bn3",
      )
      plus6 = stage2_unit4_bn3 + plus5
      stage3_unit1_bn1 = batch_normalization(
          plus6,
          variance_epsilon=1.9999999494757503e-05,
          name="stage3_unit1_bn1",
      )
      stage3_unit1_conv1sc = convolution(
          plus6,
          group=1,
          strides=[2, 2],
          padding="VALID",
          name="stage3_unit1_conv1sc",
      )
      stage3_unit1_conv1_pad = tf.pad(
          stage3_unit1_bn1, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]
      )
      stage3_unit1_conv1 = convolution(
          stage3_unit1_conv1_pad,
          group=1,
          strides=[1, 1],
          padding="VALID",
          name="stage3_unit1_conv1",
      )
      stage3_unit1_sc = batch_normalization(
          stage3_unit1_conv1sc,
          variance_epsilon=1.9999999494757503e-05,
          name="stage3_unit1_sc",
      )
      stage3_unit1_bn2 = batch_normalization(
          stage3_unit1_conv1,
          variance_epsilon=1.9999999494757503e-05,
          name="stage3_unit1_bn2",
      )
      stage3_unit1_relu1 = prelu(
          stage3_unit1_bn2, name="stage3_unit1_relu1"
      )
      stage3_unit1_conv2_pad = tf.pad(
          stage3_unit1_relu1, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]
      )
      stage3_unit1_conv2 = convolution(
          stage3_unit1_conv2_pad,
          group=1,
          strides=[2, 2],
          padding="VALID",
          name="stage3_unit1_conv2",
      )
      stage3_unit1_bn3 = batch_normalization(
          stage3_unit1_conv2,
          variance_epsilon=1.9999999494757503e-05,
          name="stage3_unit1_bn3",
      )
      plus7 = stage3_unit1_bn3 + stage3_unit1_sc
      stage3_unit2_bn1 = batch_normalization(
          plus7,
          variance_epsilon=1.9999999494757503e-05,
          name="stage3_unit2_bn1",
      )
      stage3_unit2_conv1_pad = tf.pad(
          stage3_unit2_bn1, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]
      )
      stage3_unit2_conv1 = convolution(
          stage3_unit2_conv1_pad,
          group=1,
          strides=[1, 1],
          padding="VALID",
          name="stage3_unit2_conv1",
      )
      stage3_unit2_bn2 = batch_normalization(
          stage3_unit2_conv1,
          variance_epsilon=1.9999999494757503e-05,
          name="stage3_unit2_bn2",
      )
      stage3_unit2_relu1 = prelu(
          stage3_unit2_bn2, name="stage3_unit2_relu1"
      )
      stage3_unit2_conv2_pad = tf.pad(
          stage3_unit2_relu1, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]
      )
      stage3_unit2_conv2 = convolution(
          stage3_unit2_conv2_pad,
          group=1,
          strides=[1, 1],
          padding="VALID",
          name="stage3_unit2_conv2",
      )
      stage3_unit2_bn3 = batch_normalization(
          stage3_unit2_conv2,
          variance_epsilon=1.9999999494757503e-05,
          name="stage3_unit2_bn3",
      )
      plus8 = stage3_unit2_bn3 + plus7
      stage3_unit3_bn1 = batch_normalization(
          plus8,
          variance_epsilon=1.9999999494757503e-05,
          name="stage3_unit3_bn1",
      )
      stage3_unit3_conv1_pad = tf.pad(
          stage3_unit3_bn1, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]
      )
      stage3_unit3_conv1 = convolution(
          stage3_unit3_conv1_pad,
          group=1,
          strides=[1, 1],
          padding="VALID",
          name="stage3_unit3_conv1",
      )
      stage3_unit3_bn2 = batch_normalization(
          stage3_unit3_conv1,
          variance_epsilon=1.9999999494757503e-05,
          name="stage3_unit3_bn2",
      )
      stage3_unit3_relu1 = prelu(
          stage3_unit3_bn2, name="stage3_unit3_relu1"
      )
      stage3_unit3_conv2_pad = tf.pad(
          stage3_unit3_relu1, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]
      )
      stage3_unit3_conv2 = convolution(
          stage3_unit3_conv2_pad,
          group=1,
          strides=[1, 1],
          padding="VALID",
          name="stage3_unit3_conv2",
      )
      stage3_unit3_bn3 = batch_normalization(
          stage3_unit3_conv2,
          variance_epsilon=1.9999999494757503e-05,
          name="stage3_unit3_bn3",
      )
      plus9 = stage3_unit3_bn3 + plus8
      stage3_unit4_bn1 = batch_normalization(
          plus9,
          variance_epsilon=1.9999999494757503e-05,
          name="stage3_unit4_bn1",
      )
      stage3_unit4_conv1_pad = tf.pad(
          stage3_unit4_bn1, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]
      )
      stage3_unit4_conv1 = convolution(
          stage3_unit4_conv1_pad,
          group=1,
          strides=[1, 1],
          padding="VALID",
          name="stage3_unit4_conv1",
      )
      stage3_unit4_bn2 = batch_normalization(
          stage3_unit4_conv1,
          variance_epsilon=1.9999999494757503e-05,
          name="stage3_unit4_bn2",
      )
      stage3_unit4_relu1 = prelu(
          stage3_unit4_bn2, name="stage3_unit4_relu1"
      )
      stage3_unit4_conv2_pad = tf.pad(
          stage3_unit4_relu1, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]
      )
      stage3_unit4_conv2 = convolution(
          stage3_unit4_conv2_pad,
          group=1,
          strides=[1, 1],
          padding="VALID",
          name="stage3_unit4_conv2",
      )
      stage3_unit4_bn3 = batch_normalization(
          stage3_unit4_conv2,
          variance_epsilon=1.9999999494757503e-05,
          name="stage3_unit4_bn3",
      )
      plus10 = stage3_unit4_bn3 + plus9
      stage3_unit5_bn1 = batch_normalization(
          plus10,
          variance_epsilon=1.9999999494757503e-05,
          name="stage3_unit5_bn1",
      )
      stage3_unit5_conv1_pad = tf.pad(
          stage3_unit5_bn1, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]
      )
      stage3_unit5_conv1 = convolution(
          stage3_unit5_conv1_pad,
          group=1,
          strides=[1, 1],
          padding="VALID",
          name="stage3_unit5_conv1",
      )
      stage3_unit5_bn2 = batch_normalization(
          stage3_unit5_conv1,
          variance_epsilon=1.9999999494757503e-05,
          name="stage3_unit5_bn2",
      )
      stage3_unit5_relu1 = prelu(
          stage3_unit5_bn2, name="stage3_unit5_relu1"
      )
      stage3_unit5_conv2_pad = tf.pad(
          stage3_unit5_relu1, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]
      )
      stage3_unit5_conv2 = convolution(
          stage3_unit5_conv2_pad,
          group=1,
          strides=[1, 1],
          padding="VALID",
          name="stage3_unit5_conv2",
      )
      stage3_unit5_bn3 = batch_normalization(
          stage3_unit5_conv2,
          variance_epsilon=1.9999999494757503e-05,
          name="stage3_unit5_bn3",
      )
      plus11 = stage3_unit5_bn3 + plus10
      stage3_unit6_bn1 = batch_normalization(
          plus11,
          variance_epsilon=1.9999999494757503e-05,
          name="stage3_unit6_bn1",
      )
      stage3_unit6_conv1_pad = tf.pad(
          stage3_unit6_bn1, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]
      )
      stage3_unit6_conv1 = convolution(
          stage3_unit6_conv1_pad,
          group=1,
          strides=[1, 1],
          padding="VALID",
          name="stage3_unit6_conv1",
      )
      stage3_unit6_bn2 = batch_normalization(
          stage3_unit6_conv1,
          variance_epsilon=1.9999999494757503e-05,
          name="stage3_unit6_bn2",
      )
      stage3_unit6_relu1 = prelu(
          stage3_unit6_bn2, name="stage3_unit6_relu1"
      )
      stage3_unit6_conv2_pad = tf.pad(
          stage3_unit6_relu1, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]
      )
      stage3_unit6_conv2 = convolution(
          stage3_unit6_conv2_pad,
          group=1,
          strides=[1, 1],
          padding="VALID",
          name="stage3_unit6_conv2",
      )
      stage3_unit6_bn3 = batch_normalization(
          stage3_unit6_conv2,
          variance_epsilon=1.9999999494757503e-05,
          name="stage3_unit6_bn3",
      )
      plus12 = stage3_unit6_bn3 + plus11
      stage3_unit7_bn1 = batch_normalization(
          plus12,
          variance_epsilon=1.9999999494757503e-05,
          name="stage3_unit7_bn1",
      )
      stage3_unit7_conv1_pad = tf.pad(
          stage3_unit7_bn1, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]
      )
      stage3_unit7_conv1 = convolution(
          stage3_unit7_conv1_pad,
          group=1,
          strides=[1, 1],
          padding="VALID",
          name="stage3_unit7_conv1",
      )
      stage3_unit7_bn2 = batch_normalization(
          stage3_unit7_conv1,
          variance_epsilon=1.9999999494757503e-05,
          name="stage3_unit7_bn2",
      )
      stage3_unit7_relu1 = prelu(
          stage3_unit7_bn2, name="stage3_unit7_relu1"
      )
      stage3_unit7_conv2_pad = tf.pad(
          stage3_unit7_relu1, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]
      )
      stage3_unit7_conv2 = convolution(
          stage3_unit7_conv2_pad,
          group=1,
          strides=[1, 1],
          padding="VALID",
          name="stage3_unit7_conv2",
      )
      stage3_unit7_bn3 = batch_normalization(
          stage3_unit7_conv2,
          variance_epsilon=1.9999999494757503e-05,
          name="stage3_unit7_bn3",
      )
      plus13 = stage3_unit7_bn3 + plus12
      stage3_unit8_bn1 = batch_normalization(
          plus13,
          variance_epsilon=1.9999999494757503e-05,
          name="stage3_unit8_bn1",
      )
      stage3_unit8_conv1_pad = tf.pad(
          stage3_unit8_bn1, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]
      )
      stage3_unit8_conv1 = convolution(
          stage3_unit8_conv1_pad,
          group=1,
          strides=[1, 1],
          padding="VALID",
          name="stage3_unit8_conv1",
      )
      stage3_unit8_bn2 = batch_normalization(
          stage3_unit8_conv1,
          variance_epsilon=1.9999999494757503e-05,
          name="stage3_unit8_bn2",
      )
      stage3_unit8_relu1 = prelu(
          stage3_unit8_bn2, name="stage3_unit8_relu1"
      )
      stage3_unit8_conv2_pad = tf.pad(
          stage3_unit8_relu1, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]
      )
      stage3_unit8_conv2 = convolution(
          stage3_unit8_conv2_pad,
          group=1,
          strides=[1, 1],
          padding="VALID",
          name="stage3_unit8_conv2",
      )
      stage3_unit8_bn3 = batch_normalization(
          stage3_unit8_conv2,
          variance_epsilon=1.9999999494757503e-05,
          name="stage3_unit8_bn3",
      )
      plus14 = stage3_unit8_bn3 + plus13
      stage3_unit9_bn1 = batch_normalization(
          plus14,
          variance_epsilon=1.9999999494757503e-05,
          name="stage3_unit9_bn1",
      )
      stage3_unit9_conv1_pad = tf.pad(
          stage3_unit9_bn1, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]
      )
      stage3_unit9_conv1 = convolution(
          stage3_unit9_conv1_pad,
          group=1,
          strides=[1, 1],
          padding="VALID",
          name="stage3_unit9_conv1",
      )
      stage3_unit9_bn2 = batch_normalization(
          stage3_unit9_conv1,
          variance_epsilon=1.9999999494757503e-05,
          name="stage3_unit9_bn2",
      )
      stage3_unit9_relu1 = prelu(
          stage3_unit9_bn2, name="stage3_unit9_relu1"
      )
      stage3_unit9_conv2_pad = tf.pad(
          stage3_unit9_relu1, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]
      )
      stage3_unit9_conv2 = convolution(
          stage3_unit9_conv2_pad,
          group=1,
          strides=[1, 1],
          padding="VALID",
          name="stage3_unit9_conv2",
      )
      stage3_unit9_bn3 = batch_normalization(
          stage3_unit9_conv2,
          variance_epsilon=1.9999999494757503e-05,
          name="stage3_unit9_bn3",
      )
      plus15 = stage3_unit9_bn3 + plus14
      stage3_unit10_bn1 = batch_normalization(
          plus15,
          variance_epsilon=1.9999999494757503e-05,
          name="stage3_unit10_bn1",
      )
      stage3_unit10_conv1_pad = tf.pad(
          stage3_unit10_bn1, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]
      )
      stage3_unit10_conv1 = convolution(
          stage3_unit10_conv1_pad,
          group=1,
          strides=[1, 1],
          padding="VALID",
          name="stage3_unit10_conv1",
      )
      stage3_unit10_bn2 = batch_normalization(
          stage3_unit10_conv1,
          variance_epsilon=1.9999999494757503e-05,
          name="stage3_unit10_bn2",
      )
      stage3_unit10_relu1 = prelu(
          stage3_unit10_bn2, name="stage3_unit10_relu1"
      )
      stage3_unit10_conv2_pad = tf.pad(
          stage3_unit10_relu1, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]
      )
      stage3_unit10_conv2 = convolution(
          stage3_unit10_conv2_pad,
          group=1,
          strides=[1, 1],
          padding="VALID",
          name="stage3_unit10_conv2",
      )
      stage3_unit10_bn3 = batch_normalization(
          stage3_unit10_conv2,
          variance_epsilon=1.9999999494757503e-05,
          name="stage3_unit10_bn3",
      )
      plus16 = stage3_unit10_bn3 + plus15
      stage3_unit11_bn1 = batch_normalization(
          plus16,
          variance_epsilon=1.9999999494757503e-05,
          name="stage3_unit11_bn1",
      )
      stage3_unit11_conv1_pad = tf.pad(
          stage3_unit11_bn1, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]
      )
      stage3_unit11_conv1 = convolution(
          stage3_unit11_conv1_pad,
          group=1,
          strides=[1, 1],
          padding="VALID",
          name="stage3_unit11_conv1",
      )
      stage3_unit11_bn2 = batch_normalization(
          stage3_unit11_conv1,
          variance_epsilon=1.9999999494757503e-05,
          name="stage3_unit11_bn2",
      )
      stage3_unit11_relu1 = prelu(
          stage3_unit11_bn2, name="stage3_unit11_relu1"
      )
      stage3_unit11_conv2_pad = tf.pad(
          stage3_unit11_relu1, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]
      )
      stage3_unit11_conv2 = convolution(
          stage3_unit11_conv2_pad,
          group=1,
          strides=[1, 1],
          padding="VALID",
          name="stage3_unit11_conv2",
      )
      stage3_unit11_bn3 = batch_normalization(
          stage3_unit11_conv2,
          variance_epsilon=1.9999999494757503e-05,
          name="stage3_unit11_bn3",
      )
      plus17 = stage3_unit11_bn3 + plus16
      stage3_unit12_bn1 = batch_normalization(
          plus17,
          variance_epsilon=1.9999999494757503e-05,
          name="stage3_unit12_bn1",
      )
      stage3_unit12_conv1_pad = tf.pad(
          stage3_unit12_bn1, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]
      )
      stage3_unit12_conv1 = convolution(
          stage3_unit12_conv1_pad,
          group=1,
          strides=[1, 1],
          padding="VALID",
          name="stage3_unit12_conv1",
      )
      stage3_unit12_bn2 = batch_normalization(
          stage3_unit12_conv1,
          variance_epsilon=1.9999999494757503e-05,
          name="stage3_unit12_bn2",
      )
      stage3_unit12_relu1 = prelu(
          stage3_unit12_bn2, name="stage3_unit12_relu1"
      )
      stage3_unit12_conv2_pad = tf.pad(
          stage3_unit12_relu1, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]
      )
      stage3_unit12_conv2 = convolution(
          stage3_unit12_conv2_pad,
          group=1,
          strides=[1, 1],
          padding="VALID",
          name="stage3_unit12_conv2",
      )
      stage3_unit12_bn3 = batch_normalization(
          stage3_unit12_conv2,
          variance_epsilon=1.9999999494757503e-05,
          name="stage3_unit12_bn3",
      )
      plus18 = stage3_unit12_bn3 + plus17
      stage3_unit13_bn1 = batch_normalization(
          plus18,
          variance_epsilon=1.9999999494757503e-05,
          name="stage3_unit13_bn1",
      )
      stage3_unit13_conv1_pad = tf.pad(
          stage3_unit13_bn1, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]
      )
      stage3_unit13_conv1 = convolution(
          stage3_unit13_conv1_pad,
          group=1,
          strides=[1, 1],
          padding="VALID",
          name="stage3_unit13_conv1",
      )
      stage3_unit13_bn2 = batch_normalization(
          stage3_unit13_conv1,
          variance_epsilon=1.9999999494757503e-05,
          name="stage3_unit13_bn2",
      )
      stage3_unit13_relu1 = prelu(
          stage3_unit13_bn2, name="stage3_unit13_relu1"
      )
      stage3_unit13_conv2_pad = tf.pad(
          stage3_unit13_relu1, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]
      )
      stage3_unit13_conv2 = convolution(
          stage3_unit13_conv2_pad,
          group=1,
          strides=[1, 1],
          padding="VALID",
          name="stage3_unit13_conv2",
      )
      stage3_unit13_bn3 = batch_normalization(
          stage3_unit13_conv2,
          variance_epsilon=1.9999999494757503e-05,
          name="stage3_unit13_bn3",
      )
      plus19 = stage3_unit13_bn3 + plus18
      stage3_unit14_bn1 = batch_normalization(
          plus19,
          variance_epsilon=1.9999999494757503e-05,
          name="stage3_unit14_bn1",
      )
      stage3_unit14_conv1_pad = tf.pad(
          stage3_unit14_bn1, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]
      )
      stage3_unit14_conv1 = convolution(
          stage3_unit14_conv1_pad,
          group=1,
          strides=[1, 1],
          padding="VALID",
          name="stage3_unit14_conv1",
      )
      stage3_unit14_bn2 = batch_normalization(
          stage3_unit14_conv1,
          variance_epsilon=1.9999999494757503e-05,
          name="stage3_unit14_bn2",
      )
      stage3_unit14_relu1 = prelu(
          stage3_unit14_bn2, name="stage3_unit14_relu1"
      )
      stage3_unit14_conv2_pad = tf.pad(
          stage3_unit14_relu1, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]
      )
      stage3_unit14_conv2 = convolution(
          stage3_unit14_conv2_pad,
          group=1,
          strides=[1, 1],
          padding="VALID",
          name="stage3_unit14_conv2",
      )
      stage3_unit14_bn3 = batch_normalization(
          stage3_unit14_conv2,
          variance_epsilon=1.9999999494757503e-05,
          name="stage3_unit14_bn3",
      )
      plus20 = stage3_unit14_bn3 + plus19
      stage4_unit1_bn1 = batch_normalization(
          plus20,
          variance_epsilon=1.9999999494757503e-05,
          name="stage4_unit1_bn1",
      )
      stage4_unit1_conv1sc = convolution(
          plus20,
          group=1,
          strides=[2, 2],
          padding="VALID",
          name="stage4_unit1_conv1sc",
      )
      stage4_unit1_conv1_pad = tf.pad(
          stage4_unit1_bn1, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]
      )
      stage4_unit1_conv1 = convolution(
          stage4_unit1_conv1_pad,
          group=1,
          strides=[1, 1],
          padding="VALID",
          name="stage4_unit1_conv1",
      )
      stage4_unit1_sc = batch_normalization(
          stage4_unit1_conv1sc,
          variance_epsilon=1.9999999494757503e-05,
          name="stage4_unit1_sc",
      )
      stage4_unit1_bn2 = batch_normalization(
          stage4_unit1_conv1,
          variance_epsilon=1.9999999494757503e-05,
          name="stage4_unit1_bn2",
      )
      stage4_unit1_relu1 = prelu(
          stage4_unit1_bn2, name="stage4_unit1_relu1"
      )
      stage4_unit1_conv2_pad = tf.pad(
          stage4_unit1_relu1, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]
      )
      stage4_unit1_conv2 = convolution(
          stage4_unit1_conv2_pad,
          group=1,
          strides=[2, 2],
          padding="VALID",
          name="stage4_unit1_conv2",
      )
      stage4_unit1_bn3 = batch_normalization(
          stage4_unit1_conv2,
          variance_epsilon=1.9999999494757503e-05,
          name="stage4_unit1_bn3",
      )
      plus21 = stage4_unit1_bn3 + stage4_unit1_sc
      stage4_unit2_bn1 = batch_normalization(
          plus21,
          variance_epsilon=1.9999999494757503e-05,
          name="stage4_unit2_bn1",
      )
      stage4_unit2_conv1_pad = tf.pad(
          stage4_unit2_bn1, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]
      )
      stage4_unit2_conv1 = convolution(
          stage4_unit2_conv1_pad,
          group=1,
          strides=[1, 1],
          padding="VALID",
          name="stage4_unit2_conv1",
      )
      stage4_unit2_bn2 = batch_normalization(
          stage4_unit2_conv1,
          variance_epsilon=1.9999999494757503e-05,
          name="stage4_unit2_bn2",
      )
      stage4_unit2_relu1 = prelu(
          stage4_unit2_bn2, name="stage4_unit2_relu1"
      )
      stage4_unit2_conv2_pad = tf.pad(
          stage4_unit2_relu1, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]
      )
      stage4_unit2_conv2 = convolution(
          stage4_unit2_conv2_pad,
          group=1,
          strides=[1, 1],
          padding="VALID",
          name="stage4_unit2_conv2",
      )
      stage4_unit2_bn3 = batch_normalization(
          stage4_unit2_conv2,
          variance_epsilon=1.9999999494757503e-05,
          name="stage4_unit2_bn3",
      )
      plus22 = stage4_unit2_bn3 + plus21
      stage4_unit3_bn1 = batch_normalization(
          plus22,
          variance_epsilon=1.9999999494757503e-05,
          name="stage4_unit3_bn1",
      )
      stage4_unit3_conv1_pad = tf.pad(
          stage4_unit3_bn1, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]
      )
      stage4_unit3_conv1 = convolution(
          stage4_unit3_conv1_pad,
          group=1,
          strides=[1, 1],
          padding="VALID",
          name="stage4_unit3_conv1",
      )
      stage4_unit3_bn2 = batch_normalization(
          stage4_unit3_conv1,
          variance_epsilon=1.9999999494757503e-05,
          name="stage4_unit3_bn2",
      )
      stage4_unit3_relu1 = prelu(
          stage4_unit3_bn2, name="stage4_unit3_relu1"
      )
      stage4_unit3_conv2_pad = tf.pad(
          stage4_unit3_relu1, paddings=[[0, 0], [1, 1], [1, 1], [0, 0]]
      )
      stage4_unit3_conv2 = convolution(
          stage4_unit3_conv2_pad,
          group=1,
          strides=[1, 1],
          padding="VALID",
          name="stage4_unit3_conv2",
      )
      stage4_unit3_bn3 = batch_normalization(
          stage4_unit3_conv2,
          variance_epsilon=1.9999999494757503e-05,
          name="stage4_unit3_bn3",
      )
      plus23 = stage4_unit3_bn3 + plus22
      bn1 = batch_normalization(
          plus23, variance_epsilon=1.9999999494757503e-05, name="bn1"
      )
      pre_fc1_flatten = tf.contrib.layers.flatten(bn1)
      pre_fc1 = tf.layers.dense(
          pre_fc1_flatten,
          512,
          kernel_initializer=tf.constant_initializer(
              __weights_dict["pre_fc1"]["weights"]
          ),
          bias_initializer=tf.constant_initializer(
              __weights_dict["pre_fc1"]["bias"]
          ),
          use_bias=True,
          reuse=tf.AUTO_REUSE,
      )
      fc1 = batch_normalization(
          pre_fc1, variance_epsilon=1.9999999494757503e-05, name="fc1"
      )
      return (
          data,
          fc1,
          [
              stage2_unit1_bn1,
              stage3_unit1_bn1,
              stage4_unit1_bn1,
              pre_fc1_flatten,
          ],
      )
  
imports: import tensorflow
label_desc: Use image node_id 2 for calling a global function with example usage: KitModel(weight_file, data) and returns: data,  fc1,  list