code: def visualize(tuned, no_tuned, time, method_name):
      ind = np.arange(len(tuned))
      width = 0.4
  
      fig, ax1 = plt.subplots()
  
      color = "tab:red"
      ax1.bar(
          ind, no_tuned, width, fc=(0, 0, 1, 0.5), label="No tuning"
      )
      ax1.bar(
          ind + width, tuned, width, fc=(1, 0, 0, 0.5), label="Tuned"
      )
      ax1.set_ylabel("MAE", color=color)
      ax1.tick_params(axis="y", labelcolor=color)
  
      ax2 = ax1.twinx()
  
      color = "tab:green"
      ax2.plot(ind + width / 2, time, color=color)
      ax2.set_ylabel("time, s", color=color)
      ax2.tick_params(axis="y", labelcolor=color)
  
      plt.title(f"MAE with tuning process based on {method_name}")
      fig.legend()
      fig.tight_layout()
      plt.show()
  
file_path: FEDOT/examples/advanced/time_series_forecasting/sparse_lagged_tuning.py
imports: import os
  import timeit
  import warnings
  import matplotlib.pyplot
  import numpy
  import pandas
  from golem.core.tuning.simultaneous import SimultaneousTuner
  from sklearn.metrics import mean_absolute_error
  from examples.simple.time_series_forecasting.ts_pipelines import ts_complex_dtreg_pipeline
  from fedot.core.data.data import InputData
  from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder
  from fedot.core.repository.dataset_types import DataTypesEnum
  from fedot.core.repository.metrics_repository import RegressionMetricsEnum
  from fedot.core.repository.tasks import Task, TaskTypesEnum, TsForecastingParams
  from fedot.core.utils import fedot_project_root
label_desc: Use image node_id 3 for calling a global function with example usage: visualize(tuned, no_tuned, time, method_name) without return types