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:
      layers (List): The depth of every stage.
      in_channels (int, optional): The channels of input image. Default: 3.
      embed_dims (List[int]): The width of every stage. Default: None.
      mlp_ratios (int, optional): The radio of the mlp. Default:4
      downsamples (list[bool], optional): whether use the downsamples in the model. Default: None.
      pool_size (int, optional): The kernel size of the pool layer. Default:3
      norm_layer (nn.Layer, optional): The norm layer type. Default: nn.BatchNorm2D
      act_layer (nn.Layer, optional): The activate function type. Default: nn.GELU
      num_classes (int, optional): The num of the classes. Default: 150
      down_patch_size (int, optional): The patch size of down sample. Default: 3
      down_stride (int, optional): The stride size of the down sample. Default: 2
      down_pad (int, optional): The padding size of the down sample. Default: 1
      drop_rate (float, optional): The drop rate in meta_blocks. Default: 0.
      drop_path_rate (float, optional): The drop path rate in meta_blocks. Default: 0.02
      use_layer_scale (bool, optional): Whether use the multi-scale layer. Default: True
      layer_scale_init_value (float, optional): The initial value of token. Default: 1e-5
      pretrained (str, optional): The path or url of pretrained model. Default: None.
      vit_num (int, optional): The num of vit stages. Default: 0.
      resolution (int, optional): The resolution of the input image. Default: 512.
      e_ratios (int): The ratios in the meta_blocks.
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code: def forward(self, x):
      x = self.patch_embed(x)
      x = self.forward_tokens(x)
      if self.mode is not "multi_scale":
          x = [
              paddle.concat(
                  [
                      F.interpolate(
                          feat, size=x[0].shape[-2:], mode="bilinear"
                      )
                      for feat in x
                  ],
                  axis=1,
              )
          ]
          self.feat_channels = [sum(self.feat_channels)]
      return x
  
imports: import math
  import itertools
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.utils import utils
  from paddleseg.cvlibs import manager, param_init
  from paddleseg.models.backbones.transformer_utils import *
label_desc: Use image node_id 4 for calling the EfficientFormer obj's underlying member method code with example usage: obj.forward(x) and returns: x