code: def generate_output_tables(
      input_lengths: List[int],
      num_features=5,
      overlapping=False,
      unique=True,
      main_targets=None,
  ):
      if not main_targets:
          main_targets = repeat(True)
      task = Task(TaskTypesEnum.regression)
      data_type = DataTypesEnum.table
      outputs = []
  
      # With this index step almost for sure all indices must overlap all others
      #  but yet have distinct elements that are unique for them
      idx_start = 0
      index_range_step = (
          min(input_lengths) // (len(input_lengths) + 1)
          if overlapping
          else 0
      )
      for input_len, main_target in zip(input_lengths, main_targets):
          if unique:
              idx = np.arange(idx_start, idx_start + input_len)
          else:
              # Ensure there will be repetitions in index by constraining idx_range < input_len
              idx_range = input_len // 2
              idx = np.random.randint(
                  idx_start, idx_start + idx_range, input_len
              )
          idx_start += index_range_step
  
          features = np.random.randint(
              0, input_len, (input_len, num_features)
          )
          target = (features[:, -1] ** 2).reshape(-1, 1)
          metadata = SupplementaryData(is_main_target=main_target)
  
          output_data = OutputData(
              idx,
              task,
              data_type,
              features=features,
              predict=features,
              target=target,
              supplementary_data=metadata,
          )
          outputs.append(output_data)
      return outputs
  
imports: import operator
  from itertools import product, repeat
  from typing import List
  import numpy
  import pytest
  from examples.simple.regression.regression_with_tuning import get_regression_dataset
  from fedot.core.data.data import InputData, OutputData
  from fedot.core.data.merge.data_merger import DataMerger
  from fedot.core.data.supplementary_data import SupplementaryData
  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.tasks import Task, TaskTypesEnum
label_desc: Use image node_id 4 for calling a global function with example usage: generate_output_tables(input_lengths, num_features, overlapping, unique, main_targets) and returns: outputs