code: def plot_predictions(predicted_array, test_output):
      """Function plot the predictions of the algorithm"""
      predicted_columns = np.array(predicted_array)
      actual_columns = np.array(test_output.target)
  
      # Take mean value for columns and also first and last columns
      predicted = predicted_columns.mean(axis=1)
      first_column_pr = np.ravel(predicted_columns[:, 0])
      last_column_pr = np.ravel(predicted_columns[:, -1])
      actual = actual_columns.mean(axis=1)
      first_column_act = np.ravel(actual_columns[:, 0])
      last_column_act = np.ravel(actual_columns[:, -1])
  
      plot_averaged_forecast(
          actual,
          predicted,
          first_column_act,
          last_column_act,
          first_column_pr,
          last_column_pr,
      )
  
      plot_last_day_forecast(last_column_act, last_column_pr)
  
file_path: FEDOT/cases/multi_target_levels_forecasting.py
imports: import warnings
  import numpy
  from matplotlib import pyplot
  from sklearn.metrics import mean_absolute_error
  from fedot import Fedot
  from fedot.core.data.data import InputData
  from fedot.core.data.data_split import train_test_data_setup
  from fedot.core.utils import fedot_project_root
label_desc: Use image node_id 3 for calling a global function with example usage: plot_predictions(predicted_array, test_output) without return types