code: def forward(self, x):
      return torch.stack(
          [l(x[:, i]) for i, l in enumerate(self.layers)], 1
      )
  
imports: from typing import List, Optional
  import torch
  from torch import Tensor, nn
  from ..constants import CATEGORICAL, FEATURES, LABEL, LOGITS, NUMERICAL
  from .custom_transformer import CLSToken, Custom_Transformer, _TokenInitialization
  from .utils import init_weights
label_desc: Use image node_id 2 for calling the NLinearMemoryEfficient obj's underlying member method code with example usage: obj.forward(x) and returns: torch