code: def get_border_line_info(
      idx: Any,
      predict: np.array,
      time_series: np.array,
      label: str,
      color: str = "black",
  ) -> dict:
      """
      Return plot_info for border vertical line that divides train and test part of data
  
      :param idx: idx for vertical line
      :param predict: predictions
      :param time_series: full time series with test_data
      :param label: label for a legend
      :parma color: color of a line
      """
      return {
          "idx": [idx, idx],
          "series": [
              min(np.concatenate([np.ravel(time_series), predict])),
              max(np.concatenate([np.ravel(time_series), predict])),
          ],
          "label": label,
          "color": color,
      }
  
imports: import datetime
  from typing import Any, List
  import numpy
  import pandas
  from golem.core.optimisers.genetic.gp_params import GPAlgorithmParameters
  from golem.core.optimisers.genetic.operators.mutation import MutationTypesEnum
  from fedot.core.pipelines.pipeline_composer_requirements import PipelineComposerRequirements
  from matplotlib import pyplot
  from sklearn.metrics import mean_squared_error, mean_absolute_error
  from examples.simple.time_series_forecasting.ts_pipelines import ts_complex_ridge_pipeline
  from fedot.core.composer.composer_builder import ComposerBuilder
  from fedot.core.composer.gp_composer.specific_operators import parameter_change_mutation
  from fedot.core.data.data import InputData
  from fedot.core.data.data_split import train_test_data_setup
  from fedot.core.pipelines.pipeline import Pipeline
  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 4 for calling a global function with example usage: get_border_line_info(idx, predict, time_series, label, color) and returns: dict