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
class_docstr_tok: ["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"]
code: def __init__(
      self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None
  ):
      super().__init__()
      patch_size = to_2tuple(patch_size)
      self.patch_size = patch_size
  
      self.in_chans = in_chans
      self.embed_dim = embed_dim
  
      self.proj = nn.Conv2d(
          in_chans, embed_dim, kernel_size=patch_size, stride=patch_size
      )
      if norm_layer is not None:
          self.norm = norm_layer(embed_dim)
      else:
          self.norm = None
  
imports: from collections import OrderedDict
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.checkpoint
  from torch import Tensor
  from .common import EncoderModule, _take, make_n_channel_input
  from ..activations import get_activation_block, ACT_GELU
  from timm.models.layers import DropPath, to_2tuple, trunc_normal_
label_desc: Use image node_id 1 to create a new PatchEmbed object from inherited base classes: nn with example: obj = PatchEmbed(patch_size, in_chans, embed_dim, norm_layer)