code: def __init__(
      self,
      *,
      image_size,
      patch_size,
      num_classes,
      dim,
      depth,
      heads,
      mlp_dim,
      channels=3,
      dim_head=64,
      dropout=0.0,
      emb_dropout=0.0,
      use_rotary=True,
      use_ds_conv=True,
      use_glu=True,
  ):
      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
  
      self.patch_size = patch_size
      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.cls_token = nn.Parameter(torch.randn(1, 1, dim))
      self.transformer = Transformer(
          dim,
          depth,
          heads,
          dim_head,
          mlp_dim,
          image_size,
          dropout,
          use_rotary,
          use_ds_conv,
          use_glu,
      )
  
      self.mlp_head = nn.Sequential(
          nn.LayerNorm(dim), nn.Linear(dim, num_classes)
      )
  
imports: from math import sqrt, pi, log
  import torch
  from torch import nn, einsum
  import torch.nn.functional
  from einops import rearrange, repeat
  from einops.layers.torch import Rearrange
label_desc: Use image node_id 1 to create a new RvT object from inherited base classes: nn with example: obj = RvT()