code: def get_rmse_value(
      pipeline: Pipeline, train_data: InputData, test_data: InputData
  ) -> (float, float):
      train_pred = pipeline.predict(input_data=train_data)
      test_pred = pipeline.predict(input_data=test_data)
      rmse_value_test = mse(
          y_true=test_data.target,
          y_pred=test_pred.predict,
          squared=False,
      )
      rmse_value_train = mse(
          y_true=train_data.target,
          y_pred=train_pred.predict,
          squared=False,
      )
  
      return rmse_value_train, rmse_value_test
  
imports: import numpy
  import pytest
  from sklearn.datasets import make_regression
  from sklearn.metrics import mean_squared_error
  from fedot import Fedot
  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 test.unit.common_tests import is_predict_ignores_target
  from test.unit.composer.test_metrics import data_setup
label_desc: Use image node_id 5 for calling a global function with example usage: get_rmse_value(pipeline, train_data, test_data) and returns: rmse_value_train,  rmse_value_test