class_docstr: [CLS]-token for BERT-like inference.
  
  To learn about the [CLS]-based inference, see [1].
  
  When used as a module, the [CLS]-token is appended **to the end** of each item in
  the batch.
  
  References:
  ----------
  [1] Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding" 2018
class_docstr_tok: ["[","CLS","]","-token","for","BERT-like","inference",".","To","learn","about","the","[","CLS","]","-based","inference",",","see","[","1","]",".","When","used","as","a","module",",","the","[","CLS","]","-token","is","appended","*","*","to","the","end","*","*","of","each","item","in","the","batch",".","References",":","--","--","--","--","--","[","1","]","Jacob","Devlin",",","Ming-Wei","Chang",",","Kenton","Lee",",","Kristina","Toutanova","``","BERT",":","Pre-training","of","Deep","Bidirectional","Transformers","for","Language","Understanding","''","2018"]
docstr: Append self **to the end** of each item in the batch (see `_CLSToken`).
code: def forward(self, x: Tensor) -> Tensor:
      """Append self **to the end** of each item in the batch (see `_CLSToken`)."""
      return torch.cat([x, self.expand(len(x), 1)], dim=1)
  
imports: import enum
  import math
  import warnings
  from typing import Callable, Dict, List, Optional, Tuple, Union, cast
  import torch
  import torch.nn.functional
  from torch import Tensor, nn
  from .utils import init_weights
label_desc: Use image node_id 3 for calling the CLSToken obj's underlying member method code with example usage: obj.forward(x) and returns: torch