code: def __init__(
      self,
      kernel: int,  # 3 or 5
      input_c: int,
      out_c: int,
      expanded_ratio: int,  # 1 or 6
      stride: int,  # 1 or 2
      use_se: bool,  # True
      drop_rate: float,
      index: str,  # 1a, 2a, 2b, ...
      width_coefficient: float,
  ):
      self.input_c = self.adjust_channels(input_c, width_coefficient)
      self.kernel = kernel
      self.expanded_c = self.input_c * expanded_ratio
      self.out_c = self.adjust_channels(out_c, width_coefficient)
      self.use_se = use_se
      self.stride = stride
      self.drop_rate = drop_rate
      self.index = index
  
imports: import math
  import copy
  from functools import partial
  from collections import OrderedDict
  from typing import Optional, Callable
  import torch
  import torch.nn
  from torch import Tensor
  from torch.nn import functional
label_desc: Use image node_id 1 to create a new InvertedResidualConfig object  with example: obj = InvertedResidualConfig(kernel, input_c, out_c, expanded_ratio, stride, use_se, drop_rate, index, width_coefficient)