code: def output_table_1d():
      task = Task(TaskTypesEnum.classification)
  
      samples = 1000
      x = (
          10.0
          * np.random.rand(
              samples,
          )
          - 5.0
      )
      x = np.expand_dims(x, axis=1)
      threshold = 0.5
      y = 1.0 / (1.0 + np.exp(np.power(x, -1.0)))
      classes = np.array(
          [0.0 if val <= threshold else 1.0 for val in y]
      )
      classes = np.expand_dims(classes, axis=1)
  
      data = OutputData(
          idx=np.arange(0, samples),
          features=x,
          predict=classes,
          task=task,
          data_type=DataTypesEnum.table,
      )
      return data
  
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 1 for calling a global function with example usage: output_table_1d() and returns: data