class_docstr: Based on timm and DeiT code bases
  https://github.com/rwightman/pytorch-image-models/tree/master/timm
  https://github.com/facebookresearch/deit/
class_docstr_tok: ["Based","on","timm","and","DeiT","code","bases","https",":","\/\/github.com\/rwightman\/pytorch-image-models\/tree\/master\/timm","https",":","\/\/github.com\/facebookresearch\/deit\/"]
code: def _create_xca_blocks(self, norm_layer):
      return nn.ModuleList(
          [
              self._create_xca_block(norm_layer, depth=i)
              for i in range(self.depth)
          ]
      )
  
imports: import logging
  import math
  from functools import partial
  from typing import List, Optional
  import torch
  import torch.nn
  from fairscale.nn import checkpoint_wrapper
  from vissl.config import AttrDict
  from vissl.models.model_helpers import DropPath, to_2tuple, trunc_normal_
  from vissl.models.trunks import register_model_trunk
  from vissl.models.trunks.vision_transformer import Mlp
  from vissl.utils.fsdp_utils import fsdp_wrapper
  from vissl.utils.misc import set_torch_seed
label_desc: Use image node_id 2 for calling the XCiT obj's underlying member method code with example usage: obj._create_xca_blocks(norm_layer) and returns: nn