class_docstr: Image to Patch Embedding
  
  Args:
      img_size (int): Image size.  Default: 224.
      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
code: def flops(self):
      Ho, Wo = self.patches_resolution
      flops = (
          Ho
          * Wo
          * self.embed_dim
          * self.in_chans
          * (self.patch_size[0] * self.patch_size[1])
      )
      if self.norm is not None:
          flops += Ho * Wo * self.embed_dim
      return flops
  
imports: import math
  from functools import lru_cache
  from typing import List, Optional
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.checkpoint
  from vissl.config import AttrDict
  from vissl.models.model_helpers import DropPath, rearrange, to_2tuple, trunc_normal_
  from vissl.models.trunks import register_model_trunk
label_desc: Use image node_id 4 for calling the PatchEmbed obj's underlying member method code with example usage: obj.flops() and returns: flops