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 ppdet.core.workspace import register
  from ppdet.modeling.shape_spec import ShapeSpec
  from ppdet.modeling.ops import channel_shuffle
  from ..None import layers
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)