class_docstr: Numerical tokenizer for numerical features in tabular data.
  It transforms the input numerical features to tokens (embeddings).
  
  The numerical features usually refers to continuous features.
  
  It consists of two steps:
      1. each feature is multiplied by a trainable vector i.e., weights,
      2. another trainable vector is added i.e., bias.
  
  Note that each feature has its separate pair of trainable vectors,
  i.e. the vectors are not shared between features.
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docstr: The size of one token.
code: def d_token(self) -> int:
      """The size of one token."""
      return self.weight.shape[1]
  
imports: from typing import List, Optional
  import torch
  from torch import Tensor, nn
  from ..constants import CATEGORICAL, FEATURES, LABEL, LOGITS, NUMERICAL
  from .custom_transformer import CLSToken, Custom_Transformer, _TokenInitialization
  from .utils import init_weights
label_desc: Use image node_id 3 for calling the NumericalFeatureTokenizer obj's underlying member method code with example usage: obj.d_token() and returns: self