code: def __init__(
      self,
      *,
      image_size,
      patch_size,
      num_classes,
      dim,
      depth,
      heads,
      mlp_dim,
      pool="cls",
      channels=3,
      dim_head=64,
      dropout=0.0,
      emb_dropout=0.0,
      patch_dropout=0.25,
  ):
      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(num_patches, dim))
      self.cls_token = nn.Parameter(torch.randn(1, 1, dim))
  
      self.patch_dropout = PatchDropout(patch_dropout)
      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)
      )
  
imports: import torch
  from torch import nn
  from einops import rearrange, repeat
  from einops.layers.torch import Rearrange
label_desc: Use image node_id 1 to create a new ViT object from inherited base classes: nn with example: obj = ViT()