code: def test_ransac_with_invalid_params_fit_correctly():
      """Check that on a small dataset the RANSAC anomaly search algorithm can
      adjust the values of hyperparameters
  
      As stated in the sklearn documentation, min_samples is determined by default
      based on how many features are in the dataset
      Therefore, problems can arise when there are more attributes in a dataset
      than the number of objects
      """
  
      data_path = f"{fedot_project_root()}/cases/data/cholesterol/cholesterol.csv"
  
      data = InputData.from_csv(data_path)
      train, test = train_test_data_setup(data)
      train.task.task_type = TaskTypesEnum.regression
      ransac_pipeline = get_rasnac_pipeline()
      ransac_pipeline.fit(train)
      predicted = ransac_pipeline.predict(train)
  
      assert ransac_pipeline.is_fitted
      assert predicted is not None
  
file_path: FEDOT/test/unit/data_operations/test_data_operation_params.py
imports: from copy import copy
  import numpy
  import pandas
  from fedot.core.data.data import InputData
  from fedot.core.data.data_split import train_test_data_setup
  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, TsForecastingParams
  from fedot.core.utils import fedot_project_root
label_desc: Use image node_id 4 for calling a global function with example usage: test_ransac_with_invalid_params_fit_correctly() without return types