code: def visualise(plot_info: List[dict]):
      """
      Creates a plot based on plot_info
  
      :param plot_info: list of parameters for plot:
      The possible parameters are:
              'idx' - idx (or x axis data)
              'series' - data to plot (or y axis data)
              'label' - label for legend
              'color' - (optional) color of line
      """
      plt.figure()
      for p in plot_info:
          color = p.get("color")
          plt.plot(p["idx"], p["series"], label=p["label"], color=color)
      plt.legend()
      plt.grid()
      plt.show()
  
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 3 for calling a global function with example usage: visualise(plot_info) without return types