class_docstr: Class Attention Layer as in CaiT https://arxiv.org/abs/2103.17239
class_docstr_tok: ["Class","Attention","Layer","as","in","CaiT","https",":","\/\/arxiv.org\/abs\/2103.17239"]
code: def forward(self, x, H, W, mask=None):
      x = x + self.drop_path(self.gamma1 * self.attn(self.norm1(x)))
      if self.tokens_norm:
          x = self.norm2(x)
      else:
          x[:, 0:1] = self.norm2(x[:, 0:1])
  
      x_res = x
      cls_token = x[:, 0:1]
      cls_token = self.gamma2 * self.mlp(cls_token)
      x = torch.cat([cls_token, x[:, 1:]], dim=1)
      x = x_res + self.drop_path(x)
      return x
  
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 ClassAttentionBlock obj's underlying member method code with example usage: obj.forward(x, H, W, mask) and returns: x