code: def build_ensemble(kls, **kwargs):
      """Generate ensemble of class kls."""
  
      ens = kls(**kwargs)
      ens.add(
          [
              SVR(),
              RandomForestRegressor(),
              GradientBoostingRegressor(),
              Lasso(copy_X=False),
              MLPRegressor(shuffle=False, alpha=0.001),
          ]
      )
      ens.add_meta(Lasso(copy_X=False))
      return ens
  
file_path: mlens/benchmarks/scale_ens.py
imports: import os
  import numpy
  from mlens.ensemble import SuperLearner, BlendEnsemble, Subsemble
  from mlens.utils import print_time
  from mlens.metrics import rmse
  from sklearn.ensemble import GradientBoostingRegressor
  from sklearn.linear_model import Lasso, LinearRegression
  from sklearn.neural_network import MLPRegressor
  from sklearn.ensemble import RandomForestRegressor
  from sklearn.svm import SVR
  from sklearn.base import clone
  from sklearn.datasets import make_friedman1
  from time import perf_counter
  import warnings
label_desc: Use image node_id 1 for calling a global function with example usage: build_ensemble(kls) and returns: ens