class_docstr: Image to Patch Embedding
  Args:
      img_size (int, optional): Image size.  Default: 224.
      patch_size (int, optional): Patch token size. Default: 4.
      in_chans (int, optional): Number of input image channels. Default: 3.
      embed_dim (int, optional): Number of linear projection output channels. Default: 96.
      norm_layer (nn.Layer, optional): Normalization layer. Default: None
class_docstr_tok: ["Image","to","Patch","Embedding","Args",":","img_size","(","int",",","optional",")",":","Image","size",".","Default",":","224.","patch_size","(","int",",","optional",")",":","Patch","token","size",".","Default",":","4.","in_chans","(","int",",","optional",")",":","Number","of","input","image","channels",".","Default",":","3.","embed_dim","(","int",",","optional",")",":","Number","of","linear","projection","output","channels",".","Default",":","96.","norm_layer","(","nn.Layer",",","optional",")",":","Normalization","layer",".","Default",":","None"]
code: def forward(self, x):
      B, C, H, W = x.shape
      x = self.proj(x)
  
      x = x.flatten(2).transpose([0, 2, 1])  # B Ph*Pw C
      if self.norm is not None:
          x = self.norm(x)
      return x
  
imports: import numpy
  import paddle
  import paddle.nn
  from medicalseg.cvlibs import manager, param_init
  from medicalseg.utils import load_pretrained_model
  from medicalseg.models.backbones.transformer_utils import DropPath, to_2tuple
label_desc: Use image node_id 2 for calling the PatchEmbed obj's underlying member method code with example usage: obj.forward(x) and returns: x