code: def __init__(
      self,
      input_c: int,
      kernel: int,
      expanded_c: int,
      out_c: int,
      use_se: bool,
      activation: str,
      stride: int,
      dilation: int,
      width_multi: float,
  ):
      self.input_c = self.adjust_channels(input_c, width_multi)
      self.kernel = kernel
      self.expanded_c = self.adjust_channels(expanded_c, width_multi)
      self.out_c = self.adjust_channels(out_c, width_multi)
      self.use_se = use_se
      self.use_hs = (
          activation == "HS"
      )  # whether using h-swish activation
      self.stride = stride
      self.dilation = dilation
  
imports: from typing import Callable, List, Optional
  import torch
  from torch import nn, Tensor
  from torch.nn import functional
  from functools import partial
label_desc: Use image node_id 1 to create a new InvertedResidualConfig object  with example: obj = InvertedResidualConfig(input_c, kernel, expanded_c, out_c, use_se, activation, stride, dilation, width_multi)