code: def test_pipeline_with_custom_params_for_model(data_setup):
      data = data_setup
      custom_params = dict(n_neighbors=1, weights="uniform", p=1)
  
      first = PipelineNode(operation_type="logit")
      second = PipelineNode(operation_type="lda")
      final = PipelineNode(
          operation_type="knn", nodes_from=[first, second]
      )
  
      pipeline = Pipeline(final)
      pipeline_default_params = deepcopy(pipeline)
  
      pipeline.root_node.parameters = custom_params
  
      pipeline_default_params.fit(data)
      pipeline.fit(data)
  
      custom_params_prediction = pipeline.predict(data).predict
      default_params_prediction = pipeline_default_params.predict(
          data
      ).predict
  
      assert not np.array_equal(
          custom_params_prediction, default_params_prediction
      )
  
imports: import datetime
  import os
  import platform
  import time
  from copy import deepcopy
  from multiprocessing import set_start_method
  import numpy
  import pytest
  from sklearn.datasets import load_iris
  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, TsForecastingParams
  from fedot.core.utils import probs_to_labels
  from fedot.preprocessing.preprocessing import DataPreprocessor
  from test.integration.composer.test_composer import to_categorical_codes
  from test.integration.models.test_model import classification_dataset_with_redundant_features
  from test.unit.dag.test_graph_operator import get_pipeline
  from test.unit.pipelines.test_pipeline_comparison import pipeline_first
  from test.unit.tasks.test_forecasting import get_ts_data
label_desc: Use image node_id 9 for calling a global function with example usage: test_pipeline_with_custom_params_for_model(data_setup) without return types