code: def __init__(self, n_features: int, d: int):
      super().__init__()
      self.weight = nn.Parameter(torch.ones(n_features, d))
      self.bias = nn.Parameter(torch.zeros(n_features, d))
  
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 NLayerNorm object from inherited base classes: nn with example: obj = NLayerNorm(n_features, d)