code: def _block_cfg(
      width_mult=1.0,
      depth_mult=1.0,
      initial_chs=16,
      final_chs=180,
      se_ratio=0.0,
      ch_div=1,
  ):
      layers = [1, 2, 2, 3, 3, 5]
      strides = [1, 2, 2, 2, 1, 2]
      layers = [ceil(element * depth_mult) for element in layers]
      strides = sum(
          [
              [element] + [1] * (layers[idx] - 1)
              for idx, element in enumerate(strides)
          ],
          [],
      )
      exp_ratios = [1] * layers[0] + [6] * sum(layers[1:])
      depth = sum(layers[:]) * 3
      base_chs = (
          initial_chs / width_mult if width_mult < 1.0 else initial_chs
      )
  
      # The following channel configuration is a simple instance to make each layer become an expand layer.
      out_chs_list = []
      for i in range(depth // 3):
          out_chs_list.append(
              make_divisible(
                  round(base_chs * width_mult), divisor=ch_div
              )
          )
          base_chs += final_chs / (depth // 3 * 1.0)
  
      se_ratios = [0.0] * (layers[0] + layers[1]) + [se_ratio] * sum(
          layers[2:]
      )
  
      return list(zip(out_chs_list, exp_ratios, strides, se_ratios))
  
imports: from functools import partial
  from math import ceil
  import torch
  import torch.nn
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import ClassifierHead, create_act_layer, ConvNormAct, DropPath, make_divisible, SEModule
  from ._builder import build_model_with_cfg
  from ._efficientnet_builder import efficientnet_init_weights
  from ._manipulate import checkpoint_seq
  from ._registry import generate_default_cfgs, register_model
label_desc: Use image node_id 1 for calling a global function with example usage: _block_cfg(width_mult, depth_mult, initial_chs, final_chs, se_ratio, ch_div) and returns: list