code: def __init__(
      self,
      in_channel,
      stem_channel,
      out_channel,
      expand_ratio,
      norm_type="bn",
      freeze_norm=False,
      norm_decay=0.0,
  ):
      super(Stem, self).__init__()
      self.conv1 = ConvNormLayer(
          in_channel,
          stem_channel,
          filter_size=3,
          stride=2,
          norm_type=norm_type,
          act="relu",
          freeze_norm=freeze_norm,
          norm_decay=norm_decay,
      )
      mid_channel = int(round(stem_channel * expand_ratio))
      branch_channel = stem_channel // 2
      if stem_channel == out_channel:
          inc_channel = out_channel - branch_channel
      else:
          inc_channel = out_channel - stem_channel
      self.branch1 = nn.Sequential(
          ConvNormLayer(
              ch_in=branch_channel,
              ch_out=branch_channel,
              filter_size=3,
              stride=2,
              groups=branch_channel,
              norm_type=norm_type,
              freeze_norm=freeze_norm,
              norm_decay=norm_decay,
          ),
          ConvNormLayer(
              ch_in=branch_channel,
              ch_out=inc_channel,
              filter_size=1,
              stride=1,
              norm_type=norm_type,
              act="relu",
              freeze_norm=freeze_norm,
              norm_decay=norm_decay,
          ),
      )
      self.expand_conv = ConvNormLayer(
          ch_in=branch_channel,
          ch_out=mid_channel,
          filter_size=1,
          stride=1,
          norm_type=norm_type,
          act="relu",
          freeze_norm=freeze_norm,
          norm_decay=norm_decay,
      )
      self.depthwise_conv = ConvNormLayer(
          ch_in=mid_channel,
          ch_out=mid_channel,
          filter_size=3,
          stride=2,
          groups=mid_channel,
          norm_type=norm_type,
          freeze_norm=freeze_norm,
          norm_decay=norm_decay,
      )
      self.linear_conv = ConvNormLayer(
          ch_in=mid_channel,
          ch_out=branch_channel
          if stem_channel == out_channel
          else stem_channel,
          filter_size=1,
          stride=1,
          norm_type=norm_type,
          act="relu",
          freeze_norm=freeze_norm,
          norm_decay=norm_decay,
      )
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from numbers import Integral
  from paddle import ParamAttr
  from paddle.regularizer import L2Decay
  from paddle.nn.initializer import Normal, Constant
  from paddleseg.cvlibs import manager
  from paddleseg import utils
label_desc: Use image node_id 1 to create a new Stem object from inherited base classes: nn with example: obj = Stem(in_channel, stem_channel, out_channel, expand_ratio, norm_type, freeze_norm, norm_decay)