code: def test_column_transformer_fit_transform_should_support_multiple_tuples():
      # Given
      test_case = ColumnChooserTestCase(
          data_inputs=np.array(
              [[[1, 1, 2, 3]], [[10, 11, 12, 13]], [[20, 21, 22, 23]]]
          ),
          expected_outputs=np.array(
              [[[0, 1, 2, 3]], [[10, 11, 12, 13]], [[20, 21, 22, 23]]]
          ),
          expected_processed_outputs=np.array(
              [[[2, 2, 4]], [[20, 22, 24]], [[40, 42, 44]]]
          ),
          column_transformer_tuple_list=[
              (slice(0, 2), MultiplyBy2()),
              (2, MultiplyBy2()),
          ],
          n_dimension=3,
      )
      data_inputs = test_case.data_inputs
      p = ColumnTransformer(
          test_case.column_transformer_tuple_list, test_case.n_dimension
      )
  
      # When
      p, outputs = p.fit_transform(
          data_inputs, test_case.expected_outputs
      )
  
      # Then
      assert np.array_equal(
          test_case.expected_processed_outputs, outputs
      )
      actual_fitted_data = p["2_MultiplyBy2"]["MultiplyBy2"].fitted_data
      expected_fitted_data = [
          (
              [np.array([[2]]), np.array([[12]]), np.array([[22]])],
              [
                  np.array([[0, 1, 2, 3]]),
                  np.array([[10, 11, 12, 13]]),
                  np.array([[20, 21, 22, 23]]),
              ],
          )
      ]
      assert_data_fitted_properly(
          actual_fitted_data, expected_fitted_data
      )
  
      actual_fitted_data = p["slice(0, 2, None)_MultiplyBy2"][
          "MultiplyBy2"
      ].fitted_data
      expected_fitted_data = [
          (
              [
                  np.array([[1, 1]]),
                  np.array([[10, 11]]),
                  np.array([[20, 21]]),
              ],
              [
                  np.array([[0, 1, 2, 3]]),
                  np.array([[10, 11, 12, 13]]),
                  np.array([[20, 21, 22, 23]]),
              ],
          )
      ]
      assert_data_fitted_properly(
          actual_fitted_data, expected_fitted_data
      )
  
imports: import copy
  from typing import Any, Tuple
  import numpy
  import pytest
  from neuraxle.base import BaseStep
  from neuraxle.steps.column_transformer import ColumnTransformer
label_desc: Use image node_id 5 for calling a global function with example usage: test_column_transformer_fit_transform_should_support_multiple_tuples() without return types