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_block(self, norm_layer, depth: int):
      with set_torch_seed(
          self.model_config._MODEL_INIT_SEED + depth + 1
      ):
          block = XCABlock(
              dim=self.embed_dim,
              num_heads=self.num_heads,
              mlp_ratio=self.mlp_ratio,
              qkv_bias=self.qkv_bias,
              qk_scale=self.qk_scale,
              drop=self.drop_rate,
              attn_drop=self.attn_drop_rate,
              drop_path=self.drop_path_rate,
              norm_layer=norm_layer,
              num_tokens=self.num_patches,
              eta=self.eta,
          )
          block.apply(self._init_weights)
          return block
  
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 3 for calling the XCiT obj's underlying member method code with example usage: obj._create_xca_block(norm_layer, depth) and returns: block