text: import torch
  from torch import nn
  from einops import rearrange, repeat
  from einops.layers.torch import Rearrange
  
  # helpers
  
  def pair(t):
      return t if isinstance(t, tuple) else (t, t)
  
  # classes
  
  class ViT(nn.Module):
      def __init__(self, *, image_size, patch_size, num_classes, dim, transformer, pool = 'cls', channels = 3):
          super().__init__()
  
          image_height, image_width = pair(image_size)
          patch_height, patch_width = pair(patch_size)
  
          assert image_height % patch_height == 0 and image_width % patch_width == 0, 'Image dimensions must be divisible by the patch size.'
  
          num_patches = (image_height // patch_height) * (image_width // patch_width)
          patch_dim = channels * patch_height * patch_width
  
          assert pool in {'cls', 'mean'}, 'pool type must be either cls (cls token) or mean (mean pooling)'
  
  
          self.to_patch_embedding = nn.Sequential(
              Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = patch_height, p2 = patch_width),
              nn.Linear(patch_dim, dim),
          )
  
          self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))
          self.cls_token = nn.Parameter(torch.randn(1, 1, dim))
          self.transformer = transformer
  
          self.pool = pool
          self.to_latent = nn.Identity()
  
          self.mlp_head = nn.Sequential(
              nn.LayerNorm(dim),
              nn.Linear(dim, num_classes)
          )
  
      def forward(self, img):
          x = self.to_patch_embedding(img)
          b, n, _ = x.shape
  
          cls_tokens = repeat(self.cls_token, '() n d -> b n d', b = b)
          x = torch.cat((cls_tokens, x), dim=1)
          x += self.pos_embedding[:, :(n + 1)]
          x = self.transformer(x)
  
          x = x.mean(dim = 1) if self.pool == 'mean' else x[:, 0]
  
          x = self.to_latent(x)
          return self.mlp_head(x)
  