class_docstr: Benchmarks runtime and accuracy of crypten models
  
  Models are benchmarked on synthetically generated
  Gaussian clusters for binary classification. Resnet18 is
  benchmarks use image data.
  
  Args:
      n_samples (int): number of samples for Gaussian cluster model training
      n_features (int): number of features for the Gaussian clusters.
      epochs (int): number of training epochs
      lr_rate (float): learning rate.
class_docstr_tok: ["Benchmarks","runtime","and","accuracy","of","crypten","models","Models","are","benchmarked","on","synthetically","generated","Gaussian","clusters","for","binary","classification",".","Resnet18","is","benchmarks","use","image","data",".","Args",":","n_samples","(","int",")",":","number","of","samples","for","Gaussian","cluster","model","training","n_features","(","int",")",":","number","of","features","for","the","Gaussian","clusters",".","epochs","(","int",")",":","number","of","training","epochs","lr_rate","(","float",")",":","learning","rate","."]
code: def __init__(self, device="cpu", advanced_models=False):
      self.device = torch.device(device)
      self.df = None
      self.models = models.MODELS
      if not advanced_models:
          self.remove_advanced_models()
  
imports: import argparse
  import functools
  import os
  import timeit
  from collections import namedtuple
  import crypten
  import crypten.communicator
  import numpy
  import pandas
  import torch
  from examples import multiprocess_launcher
label_desc: Use image node_id 1 to create a new ModelBenchmarks object  with example: obj = ModelBenchmarks(device, advanced_models)