code: def __init__(self, n: int, d_in: int, d_out: int, bias: bool = True):
      super().__init__()
      self.weight = nn.Parameter(Tensor(n, d_in, d_out))
      self.bias = nn.Parameter(Tensor(n, d_out)) if bias else None
      with torch.no_grad():
          for i in range(n):
              layer = nn.Linear(d_in, d_out)
              self.weight[i] = layer.weight.T
              if self.bias is not None:
                  self.bias[i] = layer.bias
  
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 1 to create a new NLinear object from inherited base classes: nn with example: obj = NLinear(n, d_in, d_out, bias)