code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      if self.hparams.deep_layers:
          x = self.linear_layers(x)
      # (N, B, E*) --> E* is the Attn Dimention
      cross_term = self.attn_proj(x).transpose(0, 1)
      if self.hparams.attention_pooling:
          attention_ops = []
      for self_attn in self.self_attns:
          cross_term, _ = self_attn(cross_term, cross_term, cross_term)
          if self.hparams.attention_pooling:
              attention_ops.append(cross_term)
      if self.hparams.attention_pooling:
          cross_term = torch.cat(attention_ops, dim=-1)
      # (B, N, E*)
      cross_term = cross_term.transpose(0, 1)
      if self.hparams.has_residuals:
          # (B, N, E*) --> Projecting Embedded input to Attention sub-space
          V_res = self.V_res_embedding(x)
          cross_term = cross_term + V_res
      # (B, NxE*)
      cross_term = nn.ReLU()(cross_term).reshape(-1, self.output_dim)
      return cross_term
  
file_path: pytorch_tabular/src/pytorch_tabular/models/autoint/autoint.py
imports: import torch
  import torch.nn
  from omegaconf import DictConfig
  from pytorch_tabular.models.common.layers import Embedding2dLayer
  from pytorch_tabular.utils import _initialize_layers, _linear_dropout_bn
  from ..base_model import BaseModel
label_desc: Use image node_id 4 for calling the AutoIntBackbone obj's underlying member method code with example usage: obj.forward(x) and returns: cross_term