class_docstr: MobileNetV3
  Args:
      config: list. MobileNetV3 depthwise blocks config.
      in_channels (int, optional): The channels of input image. Default: 3.
      scale: float=1.0. The coefficient that controls the size of network parameters. 
  Returns:
      model: nn.Layer. Specific MobileNetV3 model depends on args.
class_docstr_tok: ["MobileNetV3","Args",":","config",":","list",".","MobileNetV3","depthwise","blocks","config",".","in_channels","(","int",",","optional",")",":","The","channels","of","input","image",".","Default",":","3.","scale",":","float=1.0",".","The","coefficient","that","controls","the","size","of","network","parameters",".","Returns",":","model",":","nn.Layer",".","Specific","MobileNetV3","model","depends","on","args","."]
code: def forward(self, x):
      x = (x - self.mean) / self.std
      x = self.conv(x)
  
      feat_list = []
      for idx, block in enumerate(self.blocks):
          x = block(x)
          if idx in self.out_index:
              feat_list.append(x)
      x = self.last_second_conv(x)
      if self.return_last_conv:
          feat_list.append(x)
  
      return feat_list
  
imports: import paddle
  import paddle.nn
  from paddle import ParamAttr
  from paddle.regularizer import L2Decay
  from paddle.nn import AdaptiveAvgPool2D, BatchNorm, Conv2D, Dropout, Linear
  from paddleseg.cvlibs import manager
  from paddleseg.utils import utils, logger
  from paddleseg.models import layers
label_desc: Use image node_id 4 for calling the MobileNetV3 obj's underlying member method code with example usage: obj.forward(x) and returns: feat_list