code: def get_data_for_testing(
      task_type,
      data_type,
      length=100,
      features_count=1,
      value=0,
      random=True,
  ):
      allowed_data_type = {
          TaskTypesEnum.ts_forecasting: [
              DataTypesEnum.ts,
              DataTypesEnum.multi_ts,
          ],
          TaskTypesEnum.classification: [DataTypesEnum.table],
          TaskTypesEnum.regression: [DataTypesEnum.table],
      }
      if (
          task_type not in allowed_data_type
          or data_type not in allowed_data_type[task_type]
      ):
          return None
  
      if task_type is TaskTypesEnum.ts_forecasting:
          task = Task(
              task_type, TsForecastingParams(max(length // 10, 2))
          )
          if data_type is DataTypesEnum.ts:
              features = np.zeros(length) + value
          else:
              features = np.zeros((length, features_count)) + value
          if data_type is DataTypesEnum.table:
              target = np.zeros(length) + value
          else:
              target = features
  
      else:
          task = Task(task_type)
          data_type = DataTypesEnum.table
          features = np.zeros((length, features_count)) + value
          target = np.zeros(length) + value
          if task_type is TaskTypesEnum.classification:
              target[: int(len(target) // 2)] = 2 * value + 1
  
      if random:
          generator = np.random.RandomState(value)
          features += generator.rand(*features.shape)
          if task_type is TaskTypesEnum.ts_forecasting:
              target = features
          elif task_type is not TaskTypesEnum.classification:
              target += generator.rand(*target.shape)
  
      data = InputData(
          idx=np.arange(length),
          features=features,
          target=target,
          data_type=data_type,
          task=task,
      )
      return data
  
imports: import pickle
  from copy import deepcopy
  from time import perf_counter
  from typing import Tuple, Optional
  import numpy
  import pytest
  from golem.core.log import default_log
  from sklearn.datasets import make_classification
  from sklearn.metrics import mean_absolute_error, mean_squared_error, roc_auc_score
  from sklearn.preprocessing import MinMaxScaler
  from fedot.core.constants import FAST_TRAIN_PRESET_NAME
  from fedot.core.data.data import InputData, OutputData
  from fedot.core.data.data_split import train_test_data_setup
  from fedot.core.data.supplementary_data import SupplementaryData
  from fedot.core.operations.evaluation.operation_implementations.data_operations.sklearn_transformations import PCAImplementation
  from fedot.core.operations.evaluation.operation_implementations.models.discriminant_analysis import LDAImplementation
  from fedot.core.operations.evaluation.operation_implementations.models.ts_implementations.naive import NaiveAverageForecastImplementation, RepeatLastValueImplementation
  from fedot.core.operations.evaluation.operation_implementations.models.ts_implementations.statsmodels import GLMImplementation
  from fedot.core.operations.model import Model
  from fedot.core.operations.operation_parameters import get_default_params, OperationParameters
  from fedot.core.pipelines.node import PipelineNode
  from fedot.core.pipelines.pipeline import Pipeline
  from fedot.core.repository.dataset_types import DataTypesEnum
  from fedot.core.repository.operation_types_repository import OperationMetaInfo, OperationTypesRepository
  from fedot.core.repository.tasks import Task, TaskTypesEnum, TsForecastingParams
  from fedot.core.utils import fedot_project_root
  from test.unit.common_tests import is_predict_ignores_target
  from test.unit.data_operations.test_time_series_operations import synthetic_univariate_ts
  from test.unit.tasks.test_forecasting import get_ts_data, get_ts_data_with_dt_idx
  from test.unit.tasks.test_regression import get_synthetic_regression_data
label_desc: Use image node_id 2 for calling a global function with example usage: get_data_for_testing(task_type, data_type, length, features_count, value, random) and returns: data, None