code: def test_mixed_column_with_str_and_float_values():
      """Checks if columns with different data type ratio process correctly"""
  
      # column with index 0 must be converted to string and encoded with OHE
      train_predicted = fit_predict_cycle_for_testing(idx=0)
      assert train_predicted.features.shape[1] == 5
      assert all(
          isinstance(el, np.ndarray) for el in train_predicted.features
      )
  
      # column with index 1 must be converted to float and the gaps must be filled
      train_predicted = fit_predict_cycle_for_testing(idx=1)
      assert train_predicted.features.shape[1] == 1
      assert all(
          isinstance(el[0], float) for el in train_predicted.features
      )
  
      # column with index 2 must be removed due to unclear type of data
      try:
          _ = fit_predict_cycle_for_testing(idx=2)
      except ValueError:
          pass
  
imports: import numpy
  import pandas
  from golem.core.log import default_log
  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
  from fedot.core.utils import fedot_project_root
  from fedot.preprocessing.data_types import TYPE_TO_ID
  from fedot.preprocessing.data_types import TableTypesCorrector, apply_type_transformation
  from fedot.preprocessing.structure import DEFAULT_SOURCE_NAME
  from test.unit.preprocessing.test_pipeline_preprocessing import correct_preprocessing_params, data_with_mixed_types_in_each_column
label_desc: Use image node_id 11 for calling a global function with example usage: test_mixed_column_with_str_and_float_values() without return types