class_docstr: An Opverlaping Image to Patch Embedding Layer.
  
  Args:
      patch_size (int, optional): Patch token size. Default: 7.
      stride (int, optional): Stride of Convolution in OverlapPatchEmbed. Default: 4.
      in_chans (int, optional): Number of input image channels. Default: 3.
      embed_dim (int, optional): Number of linear projection output channels. Default: 768.
class_docstr_tok: ["An","Opverlaping","Image","to","Patch","Embedding","Layer",".","Args",":","patch_size","(","int",",","optional",")",":","Patch","token","size",".","Default",":","7.","stride","(","int",",","optional",")",":","Stride","of","Convolution","in","OverlapPatchEmbed",".","Default",":","4.","in_chans","(","int",",","optional",")",":","Number","of","input","image","channels",".","Default",":","3.","embed_dim","(","int",",","optional",")",":","Number","of","linear","projection","output","channels",".","Default",":","768","."]
code: def forward(self, x):
      x = self.proj(x)
      H, W = x.shape[2:]
      x = self.norm(x)
  
      x = x.flatten(2).transpose([0, 2, 1])
      return x, H, W
  
imports: import math
  import numpy
  import paddle
  import paddle.nn
  from paddle.nn import Conv2D
  from paddle.nn.initializer import Assign, Normal
  from paddleseg.cvlibs import manager
  from paddleseg.models.backbones.transformer_utils import DropPath, ones_, to_2tuple, zeros_
  from paddleseg.models.layers import SyncBatchNorm
  from paddleseg.utils import utils
label_desc: Use image node_id 2 for calling the OverlapPatchEmbed obj's underlying member method code with example usage: obj.forward(x) and returns: x,  H,  W