code: def forward(self, x):
      batch_size, channels, height, width = x.shape
      assert (
          height == self.img_size[0] and width == self.img_size[1]
      ), f"Input image size ({height}*{width}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
      x = self.proj(x).flatten(2).transpose(1, 2)
      return x
  
imports: import torch
  import torch.nn
  from timm.models.layers import DropPath, to_2tuple, trunc_normal_
  from typing import Optional, Callable
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