code: def __init__(
      self,
      *,
      image_size,
      patch_size,
      num_classes,
      dim,
      transformer,
      pool="cls",
      channels=3,
  ):
      super().__init__()
      image_size_h, image_size_w = pair(image_size)
      assert (
          image_size_h % patch_size == 0
          and image_size_w % patch_size == 0
      ), "image dimensions must be divisible by the patch size"
      assert pool in {
          "cls",
          "mean",
      }, "pool type must be either cls (cls token) or mean (mean pooling)"
      num_patches = (image_size_h // patch_size) * (
          image_size_w // patch_size
      )
      patch_dim = channels * patch_size**2
  
      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.LayerNorm(patch_dim),
          nn.Linear(patch_dim, dim),
          nn.LayerNorm(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)
      )
  
file_path: vit-pytorch/vit_pytorch/efficient.py
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()