code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      x = self.add_cls(x)
      for i, block in enumerate(self.transformer_blocks):
          x = block(x)
          if self.hparams.attn_feature_importance:
              self.attention_weights_[i] = block.mha.attn_weights
              # self.feature_importance_+=block.mha.attn_weights[:,:,:,-1].sum(dim=1)
              # self._calculate_feature_importance(block.mha.attn_weights)
      if self.hparams.attn_feature_importance:
          self._calculate_feature_importance()
      # Flatten (Batch, N_Categorical, Hidden) --> (Batch, N_CategoricalxHidden)
      # x = rearrange(x, "b n h -> b (n h)")
      # Taking only CLS token for the prediction head
      return x[:, -1]
  
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 4 for calling the FTTransformerBackbone obj's underlying member method code with example usage: obj.forward(x) and returns: x