code: def apply(self, x: Tensor, d: int) -> None:
      d_sqrt_inv = 1 / math.sqrt(d)
      if self == _TokenInitialization.UNIFORM:
          # used in the paper "Revisiting Deep Learning Models for Tabular Data";
          # is equivalent to `nn.init.kaiming_uniform_(x, a=math.sqrt(5))` (which is
          # used by torch to initialize nn.Linear.weight, for example)
          nn.init.uniform_(x, a=-d_sqrt_inv, b=d_sqrt_inv)
      elif self == _TokenInitialization.NORMAL:
          nn.init.normal_(x, std=d_sqrt_inv)
  
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 2 for calling the _TokenInitialization obj's underlying member method code with example usage: obj.apply(x, d) without return types