code: def __init__(
      self,
      in_features,
      out_features,
      bias=True,
      hidden_feature: int = 128,
      passport_distribute: Literal["gaussian", "uniform"] = "gaussian",
      passport_mode: Literal["single", "multi"] = "single",
      loc=-1.0,
      scale=1.0,
      low=-10.0,
      high=1.0,
      num_passport=1,
      ae_in=None,
      ae_out=None,
  ):
      super().__init__(
          passport_distribute=passport_distribute,
          passport_mode=passport_mode,
      )
  
      self._num_passport = num_passport
      self._linear = nn.Linear(in_features, hidden_feature, bias=bias)
      self._linear2 = nn.Linear(hidden_feature, out_features, bias=bias)
      if ae_in is None:
          ae_in = out_features
      if ae_out is None:
          ae_out = out_features // 4
      self._init_autoencoder(ae_in, ae_out)
      self.set_key(None, None)
      self._loc = loc
      self._scale = scale
      self._low = low
      self._high = high
  
      # running var
      self.scale, self.bias = None, None
  
imports: import random
  import torch
  from torch import nn
  import numpy
  from typing import Literal
label_desc: Use image node_id 1 to create a new LinearPassportBlock object from inherited base classes: PassportBlock with example: obj = LinearPassportBlock(in_features, out_features, bias, hidden_feature, passport_distribute, passport_mode, loc, scale, low, high, num_passport, ae_in, ae_out)