code: def domain_model_imitation_predict(
      fitted_model: any,
      idx: np.array,
      predict_data: np.array,
      params: dict,
  ):
      # TODO real custom model or more realistic imitation
      a = params.get("a")
      b = params.get("b")
      shape = predict_data.shape
      result = np.random.rand(*shape) * a + b
      # Available output_type's 'table', 'ts', 'image', 'text'
      return result, "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 1 for calling a global function with example usage: domain_model_imitation_predict(fitted_model, idx, predict_data, params) and returns: result,  str