code: def get_balanced_data_to_test_mismatch():
      task = Task(TaskTypesEnum.classification)
      x = np.array(
          [
              [0, 0, 15],
              [0, 1, 2],
              [8, 12, 0],
              [0, 1, 0],
              [1, 1, 0],
              [0, 11, 9],
              [5, 1, 10],
              [8, 16, 4],
              [3, 1, 5],
              [0, 1, 6],
              [2, 7, 9],
              [0, 1, 2],
              [14, 1, 0],
              [0, 4, 10],
          ]
      )
      y = np.array([0, 1, 2, 3, 2, 1, 0, 1, 2, 1, 0, 0, 3, 3])
      input_data = InputData(
          idx=np.arange(0, len(x)),
          features=x,
          target=y,
          task=task,
          data_type=DataTypesEnum.table,
      )
      return input_data
  
imports: from functools import partial
  from typing import Callable
  import numpy
  import pandas
  import pytest
  from fedot.core.data.data import InputData
  from fedot.core.data.data_split import train_test_data_setup
  from fedot.core.data.multi_modal import MultiModalData
  from fedot.core.optimisers.objective.data_source_splitter import DataSourceSplitter
  from fedot.core.repository.dataset_types import DataTypesEnum
  from fedot.core.repository.tasks import Task, TaskTypesEnum, TsForecastingParams
  from fedot.core.utils import split_data
  from fedot.core.data.cv_folds import cv_generator
  from test.unit.pipelines.test_decompose_pipelines import get_classification_data
  from test.unit.tasks.test_forecasting import get_ts_data
label_desc: Use image node_id 7 for calling a global function with example usage: get_balanced_data_to_test_mismatch() and returns: input_data