code: def forward(self, x: Dict):
      if self.hparams.categorical_dim > 0:
          x_cat = self.embed_input({"categorical": x["categorical"]})
      else:
          x_cat = None
      x = self.compute_backbone(
          {"categorical": x_cat, "continuous": x["continuous"]}
      )
      return self.compute_head(x)
  
imports: from collections import OrderedDict
  from typing import Dict
  import torch
  import torch.nn
  from einops import rearrange
  from omegaconf import DictConfig
  from pytorch_tabular.models.common.layers.batch_norm import BatchNorm1d
  from ..base_model import BaseModel
  from ..common.layers import Embedding2dLayer, TransformerEncoderBlock
label_desc: Use image node_id 6 for calling the TabTransformerModel obj's underlying member method code with example usage: obj.forward(x) and returns: self