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 __init__(self, patch_size=7, stride=4, in_chans=3, embed_dim=768):
      super().__init__()
      patch_size = to_2tuple(patch_size)
  
      self.proj = nn.Conv2D(
          in_chans,
          embed_dim,
          kernel_size=patch_size,
          stride=stride,
          padding=(patch_size[0] // 2, patch_size[1] // 2),
      )
      self.norm = SyncBatchNorm(embed_dim)
  
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 1 to create a new OverlapPatchEmbed object from inherited base classes: nn with example: obj = OverlapPatchEmbed(patch_size, stride, in_chans, embed_dim)