code: def fit_predict_cycle_for_testing(idx: int):
      input_data = get_mixed_data_with_str_and_float_values(idx=idx)
      train_data, test_data = train_test_data_setup(
          input_data, split_ratio=0.9, stratify=False
      )
  
      pipeline = Pipeline(PipelineNode("dt"))
      pipeline = correct_preprocessing_params(pipeline)
      train_predicted = pipeline.fit(train_data)
      return train_predicted
  
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 10 for calling a global function with example usage: fit_predict_cycle_for_testing(idx) and returns: train_predicted