code: def __init__(
      self,
      *,
      seq_len,
      patch_size,
      num_classes,
      dim,
      depth,
      heads,
      mlp_dim,
      channels=3,
      dim_head=64,
      dropout=0.0,
      emb_dropout=0.0,
  ):
      super().__init__()
      assert (seq_len % patch_size) == 0
  
      num_patches = seq_len // patch_size
      patch_dim = channels * patch_size
  
      self.to_patch_embedding = nn.Sequential(
          Rearrange("b c (n p) -> b n (p c)", p=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(dim))
      self.dropout = nn.Dropout(emb_dropout)
  
      self.transformer = Transformer(
          dim, depth, heads, dim_head, mlp_dim, dropout
      )
  
      self.mlp_head = nn.Sequential(
          nn.LayerNorm(dim), nn.Linear(dim, num_classes)
      )
  
file_path: vit-pytorch/vit_pytorch/vit_1d.py
imports: import torch
  from torch import nn
  from einops import rearrange, repeat, pack, unpack
  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()