code: def classification_dataset_with_redundant_features(
      n_samples=1000, n_features=100, n_informative=5
  ) -> InputData:
      synthetic_data = make_classification(
          n_samples=n_samples,
          n_features=n_features,
          n_informative=n_informative,
          random_state=42,
      )
  
      input_data = InputData(
          idx=np.arange(0, len(synthetic_data[1])),
          features=synthetic_data[0],
          target=synthetic_data[1],
          task=Task(TaskTypesEnum.classification),
          data_type=DataTypesEnum.table,
      )
      return input_data
  
imports: import pickle
  from copy import deepcopy
  from time import perf_counter
  from typing import Tuple, Optional
  import numpy
  import pytest
  from golem.core.log import default_log
  from sklearn.datasets import make_classification
  from sklearn.metrics import mean_absolute_error, mean_squared_error, roc_auc_score
  from sklearn.preprocessing import MinMaxScaler
  from fedot.core.constants import FAST_TRAIN_PRESET_NAME
  from fedot.core.data.data import InputData, OutputData
  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_transformations import PCAImplementation
  from fedot.core.operations.evaluation.operation_implementations.models.discriminant_analysis import LDAImplementation
  from fedot.core.operations.evaluation.operation_implementations.models.ts_implementations.naive import NaiveAverageForecastImplementation, RepeatLastValueImplementation
  from fedot.core.operations.evaluation.operation_implementations.models.ts_implementations.statsmodels import GLMImplementation
  from fedot.core.operations.model import Model
  from fedot.core.operations.operation_parameters import get_default_params, 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 OperationMetaInfo, OperationTypesRepository
  from fedot.core.repository.tasks import Task, TaskTypesEnum, TsForecastingParams
  from fedot.core.utils import fedot_project_root
  from test.unit.common_tests import is_predict_ignores_target
  from test.unit.data_operations.test_time_series_operations import synthetic_univariate_ts
  from test.unit.tasks.test_forecasting import get_ts_data, get_ts_data_with_dt_idx
  from test.unit.tasks.test_regression import get_synthetic_regression_data
label_desc: Use image node_id 9 for calling a global function with example usage: classification_dataset_with_redundant_features(n_samples, n_features, n_informative) and returns: input_data