code: def feature_importance(self):
      if self.hparams.attn_feature_importance:
          return super().feature_importance()
      else:
          raise ValueError(
              "If you want Feature Importance, `attn_feature_weights` should be `True`."
          )
  
imports: from collections import OrderedDict
  import torch
  import torch.nn
  from omegaconf import DictConfig
  from pytorch_tabular.models.common.layers.batch_norm import BatchNorm1d
  from ..base_model import BaseModel
  from ..common.layers import AppendCLSToken, Embedding2dLayer, TransformerEncoderBlock
label_desc: Use image node_id 6 for calling the FTTransformerModel obj's underlying member method code with example usage: obj.feature_importance() and returns: super