code: def __init__(
      self,
      *,
      image_size,
      num_classes,
      dim,
      depth,
      heads,
      mlp_mult,
      stages=3,
      dim_key=32,
      dim_value=64,
      dropout=0.0,
      num_distill_classes=None,
  ):
      super().__init__()
  
      dims = cast_tuple(dim, stages)
      depths = cast_tuple(depth, stages)
      layer_heads = cast_tuple(heads, stages)
  
      assert all(
          map(lambda t: len(t) == stages, (dims, depths, layer_heads))
      ), "dimensions, depths, and heads must be a tuple that is less than the designated number of stages"
  
      self.conv_embedding = nn.Sequential(
          nn.Conv2d(3, 32, 3, stride=2, padding=1),
          nn.Conv2d(32, 64, 3, stride=2, padding=1),
          nn.Conv2d(64, 128, 3, stride=2, padding=1),
          nn.Conv2d(128, dims[0], 3, stride=2, padding=1),
      )
  
      fmap_size = image_size // (2**4)
      layers = []
  
      for ind, dim, depth, heads in zip(
          range(stages), dims, depths, layer_heads
      ):
          is_last = ind == (stages - 1)
          layers.append(
              Transformer(
                  dim,
                  fmap_size,
                  depth,
                  heads,
                  dim_key,
                  dim_value,
                  mlp_mult,
                  dropout,
              )
          )
  
          if not is_last:
              next_dim = dims[ind + 1]
              layers.append(
                  Transformer(
                      dim,
                      fmap_size,
                      1,
                      heads * 2,
                      dim_key,
                      dim_value,
                      dim_out=next_dim,
                      downsample=True,
                  )
              )
              fmap_size = ceil(fmap_size / 2)
  
      self.backbone = nn.Sequential(*layers)
  
      self.pool = nn.Sequential(
          nn.AdaptiveAvgPool2d(1), Rearrange("... () () -> ...")
      )
  
      self.distill_head = (
          nn.Linear(dim, num_distill_classes)
          if exists(num_distill_classes)
          else always(None)
      )
      self.mlp_head = nn.Linear(dim, num_classes)
  
imports: from math import ceil
  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 LeViT object from inherited base classes: nn with example: obj = LeViT()