code: def mixnet_s(
      depth_multiplier=1,
      depth_divisor=8,
      min_depth=None,
      num_classes=1000,
  ):
      """
      Creates mixnet-s model.
  
      Args:
          depth_multiplier: depth_multiplier to number of filters per layer.
      """
      stem = round_filters(
          16, depth_multiplier, depth_divisor, min_depth
      )
      head = round_filters(
          1536, depth_multiplier, depth_divisor, min_depth
      )
      dropout = 0.2
  
      blocks_args = [
          "r1_k3_a1_p1_s11_e1_i16_o16",
          "r1_k3_a1.1_p1.1_s22_e6_i16_o24",
          "r1_k3_a1.1_p1.1_s11_e3_i24_o24",
          "r1_k3.5.7_a1_p1_s22_e6_i24_o40_se0.5_sw",
          "r3_k3.5_a1.1_p1.1_s11_e6_i40_o40_se0.5_sw",
          "r1_k3.5.7_a1_p1.1_s22_e6_i40_o80_se0.25_sw",
          "r2_k3.5_a1_p1.1_s11_e6_i80_o80_se0.25_sw",
          "r1_k3.5.7_a1.1_p1.1_s11_e6_i80_o120_se0.5_sw",
          "r2_k3.5.7.9_a1.1_p1.1_s11_e3_i120_o120_se0.5_sw",
          "r1_k3.5.7.9.11_a1_p1_s22_e6_i120_o200_se0.5_sw",
          "r2_k3.5.7.9_a1_p1.1_s11_e6_i200_o200_se0.5_sw",
      ]
  
      blocks_args = MixnetDecoder.decode(
          blocks_args, depth_multiplier, depth_divisor, min_depth
      )
      print("-----------")
      print("Mixnet S")
      for a in blocks_args:
          print(a)
      print("-----------")
      return MixNet(
          stem, blocks_args, head, dropout, num_classes=num_classes
      )
  
imports: import torch.nn
  from .layers import Flatten
  from .layers import SEModule
  from .layers import Swish
  from .mdconv import MDConv
  from .utils import MixnetDecoder
  from .utils import round_filters
label_desc: Use image node_id 1 for calling a global function with example usage: mixnet_s(depth_multiplier, depth_divisor, min_depth, num_classes) and returns: MixNet