code: def __init__(
      self,
      *,
      image_size,
      image_patch_size,
      frames,
      frame_patch_size,
      num_classes,
      dim,
      depth,
      heads,
      mlp_dim,
      channels=3,
      dim_head=64,
  ):
      super().__init__()
      image_height, image_width = pair(image_size)
      patch_height, patch_width = pair(image_patch_size)
  
      assert (
          image_height % patch_height == 0
          and image_width % patch_width == 0
      ), "Image dimensions must be divisible by the patch size."
      assert (
          frames % frame_patch_size == 0
      ), "Frames must be divisible by the frame patch size"
  
      num_patches = (
          (image_height // patch_height)
          * (image_width // patch_width)
          * (frames // frame_patch_size)
      )
      patch_dim = (
          channels * patch_height * patch_width * frame_patch_size
      )
  
      self.to_patch_embedding = nn.Sequential(
          Rearrange(
              "b c (f pf) (h p1) (w p2) -> b f h w (p1 p2 pf c)",
              p1=patch_height,
              p2=patch_width,
              pf=frame_patch_size,
          ),
          nn.LayerNorm(patch_dim),
          nn.Linear(patch_dim, dim),
          nn.LayerNorm(dim),
      )
  
      self.transformer = Transformer(
          dim, depth, heads, dim_head, mlp_dim
      )
  
      self.to_latent = nn.Identity()
      self.linear_head = nn.Linear(dim, num_classes)
  
imports: import torch
  import torch.nn.functional
  from torch import nn
  from einops import rearrange
  from einops.layers.torch import Rearrange
label_desc: Use image node_id 1 to create a new SimpleViT object from inherited base classes: nn with example: obj = SimpleViT()