code: def output_tables(request):
      """Generates number of tables with specified dimensions of features and predicts."""
      num_features_all = request.param
      samples = 20
  
      generated_target = np.random.sample((samples, 1))
      task = Task(TaskTypesEnum.regression)
      data_type = DataTypesEnum.table
  
      list_with_outputs = []
      for num_features in num_features_all:
          idx = np.arange(0, samples)
          generated_features = np.random.sample((samples, num_features))
          # add some random noise to predictions
          generated_predict = (
              0.1
              * np.random.sample((samples, num_features))
              * generated_target
          )
          data_info = SupplementaryData()
          output_data = OutputData(
              idx=idx,
              features=generated_features,
              predict=generated_predict,
              target=generated_target,
              task=task,
              data_type=data_type,
              supplementary_data=data_info,
          )
          list_with_outputs.append(output_data)
      return list_with_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 2 for calling a global function with example usage: output_tables(request) and returns: list_with_outputs