code: def custom_ml_model_imitation_predict(
      fitted_model: any, idx: np.array, features: np.array, params: dict
  ):
      res = fitted_model.predict(features)
      return res, "table"
  
imports: import matplotlib.pyplot
  import numpy
  import pandas
  from golem.core.tuning.simultaneous import SimultaneousTuner
  from hyperopt import hp
  from sklearn.linear_model import Ridge
  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.pipelines.tuning.search_space import PipelineSearchSpace
  from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder
  from fedot.core.repository.dataset_types import DataTypesEnum
  from fedot.core.repository.metrics_repository import RegressionMetricsEnum
  from fedot.core.repository.tasks import TaskTypesEnum, Task, TsForecastingParams
label_desc: Use image node_id 3 for calling a global function with example usage: custom_ml_model_imitation_predict(fitted_model, idx, features, params) and returns: res,  str