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 forward(self, x, out_feat_keys: Optional[List[str]] = None):
      if out_feat_keys is None or len(out_feat_keys) == 0:
          x = self.forward_features(x)
          return [x]
      else:
          return self.get_intermediate_features(x, out_feat_keys)
  
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 12 for calling the XCiT obj's underlying member method code with example usage: obj.forward(x, out_feat_keys) and returns: list, self