code: def test_one_hot_encoder_serialization():
      train_data, test_data, threshold = get_dataset("classification")
  
      pipeline = Pipeline()
      one_hot_node = PipelineNode("one_hot_encoding")
      final_node = PipelineNode("dt", nodes_from=[one_hot_node])
      pipeline.add_node(final_node)
  
      pipeline.fit(train_data)
      prediction_before_export = pipeline.predict(test_data)
  
      pipeline.save(
          "test_export_one_hot_encoding_operation", create_subdir=False
      )
  
      pipeline_after = Pipeline.from_serialized(
          create_correct_path("test_export_one_hot_encoding_operation")
      )
      prediction_after_export = pipeline_after.predict(test_data)
  
      assert np.array_equal(
          prediction_before_export.features,
          prediction_after_export.features,
      )
  
imports: import json
  import os
  import shutil
  import numpy
  import pytest
  from sklearn.metrics import mean_absolute_error
  from fedot import Fedot
  from fedot.core.pipelines.node import PipelineNode
  from fedot.core.pipelines.pipeline import Pipeline
  from fedot.core.pipelines.template import PipelineTemplate, extract_subtree_root
  from fedot.core.repository.tasks import Task, TaskTypesEnum
  from fedot.core.utils import fedot_project_root
  from test.integration.api.test_main_api import get_dataset
  from test.unit.data_operations.test_data_operations_implementations import get_mixed_data
  from test.unit.multimodal.data_generators import get_single_task_multimodal_tabular_data
  from test.unit.pipelines.test_decompose_pipelines import get_classification_data
  from test.unit.preprocessing.test_preprocessing_through_api import data_with_spaces_and_nans_in_features
  from test.unit.tasks.test_forecasting import get_multiscale_pipeline, get_simple_ts_pipeline, get_ts_data
label_desc: Use image node_id 25 for calling a global function with example usage: test_one_hot_encoder_serialization() without return types