code: def __init__(
      self,
      *,
      image_size,
      patch_size,
      freq_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(patch_size)
      freq_patch_height, freq_patch_width = pair(freq_patch_size)
  
      assert (
          image_height % patch_height == 0
          and image_width % patch_width == 0
      ), "Image dimensions must be divisible by the patch size."
      assert (
          image_height % freq_patch_height == 0
          and image_width % freq_patch_width == 0
      ), "Image dimensions must be divisible by the freq patch size."
  
      patch_dim = channels * patch_height * patch_width
      freq_patch_dim = (
          channels * 2 * freq_patch_height * freq_patch_width
      )
  
      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.LayerNorm(patch_dim),
          nn.Linear(patch_dim, dim),
          nn.LayerNorm(dim),
      )
  
      self.to_freq_embedding = nn.Sequential(
          Rearrange(
              "b c (h p1) (w p2) ri -> b (h w) (p1 p2 ri c)",
              p1=freq_patch_height,
              p2=freq_patch_width,
          ),
          nn.LayerNorm(freq_patch_dim),
          nn.Linear(freq_patch_dim, dim),
          nn.LayerNorm(dim),
      )
  
      self.pos_embedding = posemb_sincos_2d(
          h=image_height // patch_height,
          w=image_width // patch_width,
          dim=dim,
      )
  
      self.freq_pos_embedding = posemb_sincos_2d(
          h=image_height // freq_patch_height,
          w=image_width // freq_patch_width,
          dim=dim,
      )
  
      self.transformer = Transformer(
          dim, depth, heads, dim_head, mlp_dim
      )
  
      self.pool = "mean"
      self.to_latent = nn.Identity()
  
      self.linear_head = nn.Linear(dim, num_classes)
  
imports: import torch
  from torch.fft import fft2
  from torch import nn
  from einops import rearrange, reduce, pack, unpack
  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()