class_docstr: FT-Transformer for categorical tabular features.
  The input dimension is automatically computed based on
  the number of categories in each categorical column.
class_docstr_tok: ["FT-Transformer","for","categorical","tabular","features",".","The","input","dimension","is","automatically","computed","based","on","the","number","of","categories","in","each","categorical","column","."]
code: def label_key(self):
      return f"{self.prefix}_{LABEL}"
  
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 5 for calling the FT_Transformer obj's underlying member method code with example usage: obj.label_key() and returns: self, str, LABEL