code: def get_single_feature_data(task=None):
      supp_data = SupplementaryData(
          col_type_ids={
              "features": np.array([TYPE_TO_ID[int]]),
              "target": np.array([TYPE_TO_ID[int]]),
          }
      )
      train_input = InputData(
          idx=[0, 1, 2, 3, 4, 5],
          features=np.array([[1], [2], [3], [7], [8], [9]]),
          target=np.array([[0], [0], [0], [1], [1], [1]]),
          task=task,
          data_type=DataTypesEnum.table,
          supplementary_data=supp_data,
      )
  
      return train_input
  
imports: import os
  import numpy
  import pytest
  from examples.simple.classification.classification_with_tuning import get_classification_dataset
  from examples.simple.regression.regression_with_tuning import get_regression_dataset
  from examples.simple.time_series_forecasting.gapfilling import generate_synthetic_data
  from fedot.core.data.data import InputData
  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_imbalanced_class import ResampleImplementation
  from fedot.core.operations.evaluation.operation_implementations.data_operations.sklearn_transformations import ImputationImplementation
  from fedot.core.operations.evaluation.operation_implementations.data_operations.ts_transformations import CutImplementation, LaggedTransformationImplementation
  from fedot.core.operations.operation_parameters import 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 OperationTypesRepository
  from fedot.core.repository.tasks import Task, TaskTypesEnum, TsForecastingParams
  from fedot.preprocessing.data_types import TYPE_TO_ID
  from test.unit.preprocessing.test_preprocessing_through_api import data_with_only_categorical_features
label_desc: Use image node_id 6 for calling a global function with example usage: get_single_feature_data(task) and returns: train_input