code: def forward(
      self, x_cat: torch.Tensor, x_cont: torch.Tensor
  ) -> torch.Tensor:
      # (B, N)
      x = None
      if self.hparams.categorical_dim > 0:
          for i, block in enumerate(self.transformer_blocks):
              x_cat = block(x_cat)
          # Flatten (Batch, N_Categorical, Hidden) --> (Batch, N_CategoricalxHidden)
          x = rearrange(x_cat, "b n h -> b (n h)")
      if self.hparams.continuous_dim > 0:
          if self.hparams.batch_norm_continuous_input:
              x_cont = self.normalizing_batch_norm(x_cont)
          else:
              x_cont = x_cont
          # (B, N, E)
          x = x_cont if x is None else torch.cat([x, x_cont], 1)
      return 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 4 for calling the TabTransformerBackbone obj's underlying member method code with example usage: obj.forward(x_cat, x_cont) and returns: x