code: def efficientnet(
      width_coefficient=None,
      depth_coefficient=None,
      dropout_rate=0.2,
      drop_connect_rate=0.2,
      image_size=None,
      num_classes=1000,
  ):
      blocks_args = [
          "r1_k3_s11_e1_i32_o16_se0.25",
          "r2_k3_s22_e6_i16_o24_se0.25",
          "r2_k5_s22_e6_i24_o40_se0.25",
          "r3_k3_s22_e6_i40_o80_se0.25",
          "r3_k5_s11_e6_i80_o112_se0.25",
          "r4_k5_s22_e6_i112_o192_se0.25",
          "r1_k3_s11_e6_i192_o320_se0.25",
      ]
      blocks_args = BlockDecoder.decode(blocks_args)
  
      global_params = GlobalParams(
          batch_norm_momentum=0.99,
          batch_norm_epsilon=1e-3,
          dropout_rate=dropout_rate,
          drop_connect_rate=drop_connect_rate,
          num_classes=num_classes,
          width_coefficient=width_coefficient,
          depth_coefficient=depth_coefficient,
          depth_divisor=8,
          min_depth=None,
          image_size=image_size,
      )
      return blocks_args, global_params
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import math
  import re
  import collections
  import paddle
  import paddle.nn
  import paddle.nn.functional
label_desc: Use image node_id 1 for calling a global function with example usage: efficientnet(width_coefficient, depth_coefficient, dropout_rate, drop_connect_rate, image_size, num_classes) and returns: blocks_args,  global_params