code: def get_timeseries(
      length=10,
      features_count=1,
      target_count=1,
      forecast_length=_FORECAST_LENGTH,
      random=False,
  ):
      task = Task(
          TaskTypesEnum.ts_forecasting,
          TsForecastingParams(forecast_length=forecast_length),
      )
      if random:
          features = np.random.rand(length, features_count) * 10
          features = (
              features.ravel() if features_count == 1 else features
          )
          target = deepcopy(features)
      else:
          features = np.arange(0, length * features_count) * 10
          target = np.arange(0, length * target_count) * 100
      if features_count > 1:
          features = np.reshape(features, (features_count, length)).T
          for i in range(features_count):
              features[:, i] += i
  
      if target_count > 1:
          target = np.reshape(target, (target_count, length)).T
  
      train_input = InputData(
          idx=np.arange(0, length),
          features=features,
          target=target,
          task=task,
          data_type=DataTypesEnum.ts,
      )
      return train_input
  
imports: import logging
  from copy import deepcopy
  import numpy
  import pytest
  from golem.core.log import default_log
  from fedot.core.data.data import InputData
  from fedot.core.data.data_split import train_test_data_setup
  from fedot.core.data.multi_modal import MultiModalData
  from fedot.core.operations.evaluation.operation_implementations.data_operations.ts_transformations import _sparse_matrix, prepare_target, ts_to_table
  from fedot.core.optimisers.objective import MetricsObjective, PipelineObjectiveEvaluate
  from fedot.core.optimisers.objective.data_source_splitter import DataSourceSplitter
  from fedot.core.pipelines.node import PipelineNode
  from fedot.core.pipelines.pipeline import Pipeline
  from fedot.core.pipelines.pipeline_builder import PipelineBuilder
  from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder
  from fedot.core.repository.dataset_types import DataTypesEnum
  from fedot.core.repository.tasks import Task, TaskTypesEnum, TsForecastingParams
label_desc: Use image node_id 4 for calling a global function with example usage: get_timeseries(length, features_count, target_count, forecast_length, random) and returns: train_input