code: def __init__(
      self,
      in_channels,
      cfgs,
      stem,
      inp_channel=16,
      activation=nn.ReLU,
      width_mult=1.0,
  ):
      super().__init__()
  
      self.stem = stem
      if stem:
          self.stem_block = nn.Sequential(
              layers.ConvBN(
                  in_channels,
                  inp_channel,
                  3,
                  stride=2,
                  padding=1,
                  bias_attr=False,
              ),
              activation(),
          )
      self.cfgs = cfgs
  
      self.layers = []
      for i, (k, t, c, s) in enumerate(cfgs):
          output_channel = _make_divisible(c * width_mult, 8)
          exp_size = t * inp_channel
          exp_size = _make_divisible(exp_size * width_mult, 8)
          layer_name = "layer{}".format(i + 1)
          layer = InvertedResidual(
              inp_channel,
              output_channel,
              ks=k,
              stride=s,
              expand_ratio=t,
              activations=activation,
          )
          self.add_sublayer(layer_name, layer)
          inp_channel = output_channel
          self.layers.append(layer_name)
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager, param_init
  from paddleseg.models import layers
  from paddleseg.models.backbones.transformer_utils import DropPath, zeros_, ones_
  from paddleseg.utils import utils
label_desc: Use image node_id 1 to create a new StackedMV2Block object from inherited base classes: nn with example: obj = StackedMV2Block(in_channels, cfgs, stem, inp_channel, activation, width_mult)