code: def get_unbalanced_dataset(size=10, disbalance=0.4, target_dim=None):
      """Generate table with one numerical and one categorical features by selected size and class disbalance
      Target is binary and unbalanced: majority "1" class is more than minority "0" class.
      It can be generated with two options: 1D or 2D representation of target.
      """
      minor_size = round(size * disbalance)
      major_size = size - minor_size
  
      features = np.array(
          [[np.random.rand(), "minor"]] * minor_size
          + [[np.random.rand(), "major"]] * major_size
      )
      target = np.array([0] * minor_size + [1] * major_size)
  
      if target_dim == 2:
          target = target.reshape(-1, 1)
  
      supp_data = SupplementaryData(
          col_type_ids={
              "features": np.array([TYPE_TO_ID[int], TYPE_TO_ID[str]]),
              "target": np.array([TYPE_TO_ID[int]]),
          }
      )
  
      input_data = InputData(
          idx=np.arange(features.shape[0]),
          features=features,
          target=target,
          task=Task(TaskTypesEnum.classification),
          data_type=DataTypesEnum.table,
          supplementary_data=supp_data,
      )
  
      return input_data
  
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 9 for calling a global function with example usage: get_unbalanced_dataset(size, disbalance, target_dim) and returns: input_data