old_contents: """ Image to Patch Embedding using Conv2d
  
  A convolution based approach to patchifying a 2D image w/ embedding projection.
  
  Based on the impl in https://github.com/google-research/vision_transformer
  
  Hacked together by / Copyright 2020 Ross Wightman
  """
  import torch
  from torch import nn as nn
  
  from .helpers import to_2tuple
  
  
  class PatchEmbed(nn.Module):
      """ 2D Image to Patch Embedding
      """
      def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, norm_layer=None, flatten=True):
          super().__init__()
          img_size = to_2tuple(img_size)
          patch_size = to_2tuple(patch_size)
          self.img_size = img_size
          self.patch_size = patch_size
          self.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1])
          self.num_patches = self.grid_size[0] * self.grid_size[1]
          self.flatten = flatten
  
          self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
          self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
  
      def forward(self, x):
          B, C, H, W = x.shape
          torch._assert(H == self.img_size[0], f"Input image height ({H}) doesn't match model {self.img_size[0]}.")
          torch._assert(W == self.img_size[1], f"Input image width ({W}) doesn't match model ({self.img_size[1]}.")
          x = self.proj(x)
          if self.flatten:
              x = x.flatten(2).transpose(1, 2)  # BCHW -> BNC
          x = self.norm(x)
          return x
  
new_contents: """ Image to Patch Embedding using Conv2d
  
  A convolution based approach to patchifying a 2D image w/ embedding projection.
  
  Based on the impl in https://github.com/google-research/vision_transformer
  
  Hacked together by / Copyright 2020 Ross Wightman
  """
  import torch
  from torch import nn as nn
  
  from .helpers import to_2tuple
  
  
  class PatchEmbed(nn.Module):
      """ 2D Image to Patch Embedding
      """
      def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, norm_layer=None, flatten=True):
          super().__init__()
          img_size = to_2tuple(img_size)
          patch_size = to_2tuple(patch_size)
          self.img_size = img_size
          self.patch_size = patch_size
          self.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1])
          self.num_patches = self.grid_size[0] * self.grid_size[1]
          self.flatten = flatten
  
          self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
          self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
  
      def forward(self, x):
          B, C, H, W = x.shape
          torch._assert(H == self.img_size[0], f"Input image height ({H}) doesn't match model ({self.img_size[0]}).")
          torch._assert(W == self.img_size[1], f"Input image width ({W}) doesn't match model ({self.img_size[1]}).")
          x = self.proj(x)
          if self.flatten:
              x = x.flatten(2).transpose(1, 2)  # BCHW -> BNC
          x = self.norm(x)
          return x
  
subject: Fix formatting of last commit