class_docstr: Represents datasets we use for expriments
docstr: find the optimal weight vector for the logistic regression
  
      for the problems with real datasets.
  
  dataset = training dataset
  bias = bias for the logistic model
docstr_tok: ["find","the","optimal","weight","vector","for","the","logistic","regression","for","the","problems","with","real","datasets",".","dataset","=","training","dataset","bias","=","bias","for","the","logistic","model"]
code: def find_optimal_classifier(self, dataset, bias=True):
      """find the optimal weight vector for the logistic regression
  
          for the problems with real datasets.
  
      dataset = training dataset
      bias = bias for the logistic model
      """
      inputs_vec, labels = dataset
      reg = 1e-9
      if bias is True:
          model_lr = LogisticRegression(max_iter=200, C=1 / reg).fit(
              inputs_vec, labels
          )
          w_opt1 = np.concatenate(
              [model_lr.intercept_, np.squeeze(model_lr.coef_)]
          )
          w_opt = newton(dataset, w_opt1, bias)
      else:
          model_lr = LogisticRegression(
              max_iter=200, fit_intercept=False, C=1 / reg
          ).fit(inputs_vec, labels)
          w_opt1 = np.squeeze(model_lr.coef_)
          w_opt = newton(dataset, w_opt1, bias)
      return w_opt
  
imports: import os
  import ssl
  import tarfile
  import urllib.request
  from my_logistic_regression import MyLogisticRegression
  import numpy
  import requests
  from sklearn import preprocessing
  import sklearn.datasets
  from sklearn.linear_model import LogisticRegression
  import torch
  from torchvision import datasets, transforms
label_desc: Use image node_id 2 for calling the Mydatasets obj's underlying member method code with example usage: obj.find_optimal_classifier(dataset, bias) and returns: w_opt