class_docstr: Vision transformer with locally-initializable self-attention layers
class_docstr_tok: ["Vision","transformer","with","locally-initializable","self-attention","layers"]
code: def forward(self, x, out_feat_keys):
      x = self.forward_features(x)
      x = x.unsqueeze(0)
      return x
  
imports: import copy
  import logging
  import math
  import torch
  import torch.nn
  from vissl.models.model_helpers import DropPath, to_2tuple, trunc_normal_
  from vissl.models.trunks import register_model_trunk
  from vissl.utils.hydra_config import AttrDict
label_desc: Use image node_id 5 for calling the ConViT obj's underlying member method code with example usage: obj.forward(x, out_feat_keys) and returns: x