code: def __init__(self, k):
      super(SCORE_MODEL, self).__init__()
      self.hidden_size = 256
      self.network = nn.Sequential(
          nn.Linear(k, self.hidden_size),
          nn.Tanh(),
          nn.Linear(self.hidden_size, self.hidden_size),
          nn.Tanh(),
          nn.Linear(self.hidden_size, self.hidden_size),
          nn.Tanh(),
          nn.Linear(self.hidden_size, 1),
          nn.Sigmoid(),
      )
  
imports: from copy import deepcopy
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from sklearn.metrics import roc_auc_score
  from sklearn.model_selection import train_test_split
  from sklearn.neighbors import NearestNeighbors
  from sklearn.preprocessing import MinMaxScaler
  from sklearn.utils.validation import check_is_fitted
  from .base import BaseDetector
label_desc: Use image node_id 1 to create a new SCORE_MODEL object from inherited base classes: nn with example: obj = SCORE_MODEL(k)