code: def forward(self, x):
      assert (
          x.ndim == 3
      ), "Error input dimension, should be 3, but given {}".format(
          x.ndim
      )
      x = x[..., None] * self.weight[None]
      x = x.sum(-2)
      if self.bias is not None:
          x = x + self.bias[None]
      return x
  
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 NLinear obj's underlying member method code with example usage: obj.forward(x) and returns: x