text: import torch
  from torch import nn, einsum
  import torch.nn.functional as F
  
  from einops import rearrange, repeat
  from einops.layers.torch import Rearrange
  
  class Residual(nn.Module):
      def __init__(self, fn):
          super().__init__()
          self.fn = fn
      def forward(self, x, **kwargs):
          return self.fn(x, **kwargs) + x
  
  class PreNorm(nn.Module):
      def __init__(self, dim, fn):
          super().__init__()
          self.norm = nn.LayerNorm(dim)
          self.fn = fn
      def forward(self, x, **kwargs):
          return self.fn(self.norm(x), **kwargs)
  
  class FeedForward(nn.Module):
      def __init__(self, dim, hidden_dim, dropout = 0.):
          super().__init__()
          self.net = nn.Sequential(
              nn.Linear(dim, hidden_dim),
              nn.GELU(),
              nn.Dropout(dropout),
              nn.Linear(hidden_dim, dim),
              nn.Dropout(dropout)
          )
      def forward(self, x):
          return self.net(x)
  
  class Attention(nn.Module):
      def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0.):
          super().__init__()
          inner_dim = dim_head *  heads
          project_out = not (heads == 1 and dim_head == dim)
  
          self.heads = heads
          self.scale = dim_head ** -0.5
  
          self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)
  
          self.to_out = nn.Sequential(
              nn.Linear(inner_dim, dim),
              nn.Dropout(dropout)
          ) if project_out else nn.Identity()
  
      def forward(self, x):
          b, n, _, h = *x.shape, self.heads
          qkv = self.to_qkv(x).chunk(3, dim = -1)
          q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), qkv)
  
          dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale
  
          attn = dots.softmax(dim=-1)
  
          out = einsum('b h i j, b h j d -> b h i d', attn, v)
          out = rearrange(out, 'b h n d -> b n (h d)')
          out =  self.to_out(out)
          return out
  
  class Transformer(nn.Module):
      def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout = 0.):
          super().__init__()
          self.layers = nn.ModuleList([])
          for _ in range(depth):
              self.layers.append(nn.ModuleList([
                  Residual(PreNorm(dim, Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout))),
                  Residual(PreNorm(dim, FeedForward(dim, mlp_dim, dropout = dropout)))
              ]))
      def forward(self, x):
          for attn, ff in self.layers:
              x = attn(x)
              x = ff(x)
          return x
  
  class ViT(nn.Module):
      def __init__(self, *, image_size, patch_size, num_classes, dim, depth, heads, mlp_dim, pool = 'cls', channels = 3, dim_head = 64, dropout = 0., emb_dropout = 0.):
          super().__init__()
          assert image_size % patch_size == 0, 'Image dimensions must be divisible by the patch size.'
          num_patches = (image_size // patch_size) ** 2
          patch_dim = channels * patch_size ** 2
          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_size, p2 = patch_size),
              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.dropout = nn.Dropout(emb_dropout)
  
          self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, dropout)
  
          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.dropout(x)
  
          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)
  