code: def __init__(
      self,
      *,
      image_size,
      patch_size,
      num_classes,
      dim,
      depth,
      heads,
      mlp_dim,
      dim_head=64,
      dropout=0.0,
      emb_dropout=0.0,
      channels=3,
  ):
      super().__init__()
      assert (
          image_size % patch_size == 0
      ), "Image dimensions must be divisible by the patch size."
      assert isinstance(
          depth, tuple
      ), "depth must be a tuple of integers, specifying the number of blocks before each downsizing"
      heads = cast_tuple(heads, len(depth))
  
      patch_dim = channels * patch_size**2
  
      self.to_patch_embedding = nn.Sequential(
          nn.Unfold(kernel_size=patch_size, stride=patch_size // 2),
          Rearrange("b c n -> b n c"),
          nn.Linear(patch_dim, dim),
      )
  
      output_size = conv_output_size(
          image_size, patch_size, patch_size // 2
      )
      num_patches = output_size**2
  
      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(emb_dropout)
  
      layers = []
  
      for ind, (layer_depth, layer_heads) in enumerate(
          zip(depth, heads)
      ):
          not_last = ind < (len(depth) - 1)
  
          layers.append(
              Transformer(
                  dim,
                  layer_depth,
                  layer_heads,
                  dim_head,
                  mlp_dim,
                  dropout,
              )
          )
  
          if not_last:
              layers.append(Pool(dim))
              dim *= 2
  
      self.layers = nn.Sequential(*layers)
  
      self.mlp_head = nn.Sequential(
          nn.LayerNorm(dim), nn.Linear(dim, num_classes)
      )
  
imports: from math import sqrt
  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 PiT object from inherited base classes: nn with example: obj = PiT()