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: Args:
      d_token: the size of token
      initialization: initialization policy for parameters. Must be one of
          :code:`['uniform', 'normal']`. Let :code:`s = d ** -0.5`. Then, the
          corresponding distributions are :code:`Uniform(-s, s)` and :code:`Normal(0, s)`. In
          the paper [gorishniy2021revisiting], the 'uniform' initialization was
          used.
  
  References:
      * [gorishniy2021revisiting] Yury Gorishniy, Ivan Rubachev, Valentin Khrulkov, Artem Babenko "Revisiting Deep Learning Models for Tabular Data", 2021
code: def __init__(self, d_token: int, initialization: str) -> None:
      """
      Args:
          d_token: the size of token
          initialization: initialization policy for parameters. Must be one of
              :code:`['uniform', 'normal']`. Let :code:`s = d ** -0.5`. Then, the
              corresponding distributions are :code:`Uniform(-s, s)` and :code:`Normal(0, s)`. In
              the paper [gorishniy2021revisiting], the 'uniform' initialization was
              used.
  
      References:
          * [gorishniy2021revisiting] Yury Gorishniy, Ivan Rubachev, Valentin Khrulkov, Artem Babenko "Revisiting Deep Learning Models for Tabular Data", 2021
      """
      super().__init__()
      initialization_ = _TokenInitialization.from_str(initialization)
      self.weight = nn.Parameter(Tensor(d_token))
      initialization_.apply(self.weight, d_token)
  
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 1 to create a new CLSToken object from inherited base classes: nn with example: obj = CLSToken(d_token, initialization)