class_docstr: Feature tokenizer for categorical features in tabular data.
  It transforms the input categorical features to tokens (embeddings).
  
  The categorical features usually refers to discrete features.
class_docstr_tok: ["Feature","tokenizer","for","categorical","features","in","tabular","data",".","It","transforms","the","input","categorical","features","to","tokens","(","embeddings",")",".","The","categorical","features","usually","refers","to","discrete","features","."]
docstr: The size of one token.
code: def d_token(self) -> int:
      """The size of one token."""
      return self.embeddings.embedding_dim
  
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 CategoricalFeatureTokenizer obj's underlying member method code with example usage: obj.d_token() and returns: self