class_docstr: The MobileNetV2 implementation based on PaddlePaddle.
  
  The original article refers to
  Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen
  "MobileNetV2: Inverted Residuals and Linear Bottlenecks"
  (https://arxiv.org/abs/1801.04381).
  
  Args:
      scale (float, optional): The scale of channel. Default: 1.0
      in_channels (int, optional): The channels of input image. Default: 3.
      pretrained (str, optional): The path or url of pretrained model. Default: None
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code: def forward(self, inputs):
      feat_list = []
  
      y = self.conv1(inputs, if_act=True)
      for idx, block in enumerate(self.block_list):
          y = block(y)
          if idx in self.out_index:
              feat_list.append(y)
  
      return feat_list
  
imports: 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.cvlibs import manager
  from paddleseg import utils
label_desc: Use image node_id 2 for calling the MobileNetV2 obj's underlying member method code with example usage: obj.forward(inputs) and returns: feat_list