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","."]
docstr: All layers have the same id 0 since there is no pre-trained models used here.
  
  Returns
  -------
  A dictionary mapping the layer names (keys) to their ids (values).
code: def get_layer_ids(
      self,
  ):
      """
      All layers have the same id 0 since there is no pre-trained models used here.
  
      Returns
      -------
      A dictionary mapping the layer names (keys) to their ids (values).
      """
      name_to_id = {}
      for n, _ in self.named_parameters():
          name_to_id[n] = 0
  
      return name_to_id
  
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 7 for calling the FT_Transformer obj's underlying member method code with example usage: obj.get_layer_ids() and returns: name_to_id