class_docstr: Networj of MobileNet
class_docstr_tok: ["Networj","of","MobileNet"]
code: def __init__(
      self,
      input_channels: int = 3,
      scale: float = 1.0,
      pretrained: str = None,
      prefix_name: str = "",
  ):
      super(MobileNet, self).__init__()
      self.scale = scale
  
      bottleneck_params_list = [
          (1, 16, 1, 1),
          (6, 24, 2, 2),
          (6, 32, 3, 2),
          (6, 64, 4, 2),
          (6, 96, 3, 1),
          (6, 160, 3, 2),
          (6, 320, 1, 1),
      ]
  
      self.conv1 = ConvBNLayer(
          num_channels=input_channels,
          num_filters=int(32 * scale),
          filter_size=3,
          stride=2,
          padding=1,
          name=prefix_name + "conv1_1",
      )
  
      self.block_list = []
      i = 1
      in_c = int(32 * scale)
      for layer_setting in bottleneck_params_list:
          t, c, n, s = layer_setting
          i += 1
          block = self.add_sublayer(
              prefix_name + "conv" + str(i),
              sublayer=InvresiBlocks(
                  in_c=in_c,
                  t=t,
                  c=int(c * scale),
                  n=n,
                  s=s,
                  name=prefix_name + "conv" + str(i),
              ),
          )
          self.block_list.append(block)
          in_c = int(c * scale)
  
      self.out_c = int(1280 * scale) if scale > 1.0 else 1280
      self.conv9 = ConvBNLayer(
          num_channels=in_c,
          num_filters=self.out_c,
          filter_size=1,
          stride=1,
          padding=0,
          name=prefix_name + "conv9",
      )
  
      self.feat_channels = [
          int(i * scale) for i in [16, 24, 32, 96, 1280]
      ]
      self.pretrained = pretrained
  
imports: import math
  import numpy
  import paddle
  from paddle import ParamAttr
  import paddle.nn
  import paddle.nn.functional
  from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
  from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
  from paddleseg import utils
  from paddleseg.cvlibs import manager
label_desc: Use image node_id 1 to create a new MobileNet object from inherited base classes: nn with example: obj = MobileNet(input_channels, scale, pretrained, prefix_name)