code: def __init__(self, *, dim, image_size, patch_size, dropout=0.0):
      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 = 3 * 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.dropout = nn.Dropout(dropout)
  
imports: 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 ImageEmbedder object from inherited base classes: nn with example: obj = ImageEmbedder()