class_docstr: The EfficientFormerV2 implementation based on PaddlePaddle.
  
  The original article refers to Yanyu Li, Ju Hu, Yang Wen, Georgios Evangelidis, 
  Kamyar Salahi, Yanzhi Wang, Sergey Tulyakov, Jian Ren. 
  "Rethinking Vision Transformers for MobileNet Size and Speed". 
  (https://arxiv.org/pdf/2212.08059.pdf).
  
  Args:
      backbone (paddle.nn.Layer): Backbone networks.
      num_classes (int): The unique number of target classes.
      backbone_indices (list[int], optional): The values in the tuple indicate the indices of output of backbone.
          Default: [0, 1, 2, 3].
      align_corners (bool, optional): An argument of F.interpolate. It should be set to False when the output size of feature
          is even, e.g. 1024x512, otherwise it is True, e.g. 769x769.  Default: False.
      head_channels (int, optional): The number of hidden channels of segmentation head. Default: 128.
      add_extra_convs (bool|str): An argument about whether to add extra conv or not or where to add extra conv. Default: False.
      pretrained (str, optional): The path or url of pretrained model. Default: None.
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code: def init_weight(self):
      if self.pretrained is not None:
          utils.load_entire_model(self, self.pretrained)
  
imports: import numpy
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager, param_init
  from paddleseg.utils import utils
label_desc: Use image node_id 3 for calling the EfficientFormerSeg obj's underlying member method code with example usage: obj.init_weight() without return types