code: def __init__(
      self,
      dim,
      depth,
      heads,
      dim_head,
      mlp_dim,
      local_patch_kernel_size=3,
      dropout=0.0,
      layer_dropout=0.0,
  ):
      super().__init__()
      self.layers = ModuleList([])
      self.layer_dropout = layer_dropout
  
      for ind in range(depth):
          layer = ind + 1
          self.layers.append(
              ModuleList(
                  [
                      LayerScale(
                          dim,
                          XCAttention(
                              dim,
                              heads=heads,
                              dim_head=dim_head,
                              dropout=dropout,
                          ),
                          depth=layer,
                      ),
                      LayerScale(
                          dim,
                          LocalPatchInteraction(
                              dim, local_patch_kernel_size
                          ),
                          depth=layer,
                      ),
                      LayerScale(
                          dim,
                          FeedForward(dim, mlp_dim, dropout=dropout),
                          depth=layer,
                      ),
                  ]
              )
          )
  
imports: from random import randrange
  import torch
  from torch import nn, einsum
  from torch.nn import Module, ModuleList
  import torch.nn.functional
  from einops import rearrange, repeat, pack, unpack
  from einops.layers.torch import Rearrange
label_desc: Use image node_id 1 to create a new XCATransformer object from inherited base classes: Module with example: obj = XCATransformer(dim, depth, heads, dim_head, mlp_dim, local_patch_kernel_size, dropout, layer_dropout)