class_docstr: Image to Patch Embedding
  Args:
      patch_size (int): Patch token size. Default: 4.
      in_chans (int): Number of input image channels. Default: 3.
      embed_dim (int): Number of linear projection output channels. Default: 96.
      norm_layer (nn.Module, optional): Normalization layer. Default: None
docstr: Forward function.
code: def forward(self, x):
      """Forward function."""
      # padding
      _, _, H, W = x.size()
      if W % self.patch_size[1] != 0:
          x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))
      if H % self.patch_size[0] != 0:
          x = F.pad(
              x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0])
          )
  
      x = self.proj(x)  # B C Wh Ww
      if self.norm is not None:
          Wh, Ww = x.size(2), x.size(3)
          x = x.flatten(2).transpose(1, 2)
          x = self.norm(x)
          x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)
  
      return x
  
imports: import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.checkpoint
  from detectron2.modeling.backbone.backbone import Backbone
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