code: def configure_experiment():
      """Generates a time series of 100 elements. The prediction is performed
      for five elements ahead
      """
      # Default number of validation blocks
      validation_blocks = 3
      forecast_len = 5
  
      time_series, _ = get_ts_data(
          n_steps=105, forecast_length=forecast_len
      )
  
      return forecast_len, validation_blocks, time_series
  
imports: import datetime
  import logging
  from hyperopt import hp
  from golem.core.log import default_log
  from golem.core.optimisers.genetic.gp_params import GPAlgorithmParameters
  from golem.core.tuning.simultaneous import SimultaneousTuner
  from fedot.core.pipelines.pipeline_composer_requirements import PipelineComposerRequirements
  from examples.advanced.time_series_forecasting.composing_pipelines import get_available_operations
  from fedot import Fedot
  from fedot.core.composer.composer_builder import ComposerBuilder
  from fedot.core.pipelines.pipeline import Pipeline
  from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder
  from fedot.core.repository.metrics_repository import MetricsRepository, RegressionMetricsEnum
  from fedot.core.repository.tasks import TsForecastingParams
  from fedot.core.pipelines.tuning.search_space import PipelineSearchSpace
  from fedot.core.data.cv_folds import cv_generator
  from test.unit.tasks.test_forecasting import get_simple_ts_pipeline, get_ts_data
label_desc: Use image node_id 1 for calling a global function with example usage: configure_experiment() and returns: forecast_len,  validation_blocks,  time_series