code: def test_cnn_methods():
      (
          _,
          dataset_to_train,
          dataset_to_validate,
      ) = get_image_classification_data()
      image_shape = (28, 28, 1)
      num_classes = 7
      epochs = 10
      batch_size = 128
  
      cnn_model = create_deep_cnn(
          input_shape=image_shape, num_classes=num_classes
      )
  
      transformed_x_train, transform_flag = check_input_array(
          x_train=dataset_to_train.features
      )
  
      model = fit_cnn(
          train_data=dataset_to_train,
          model=cnn_model,
          epochs=epochs,
          batch_size=batch_size,
      )
  
      prediction = predict_cnn(
          trained_model=model, predict_data=dataset_to_validate
      )
  
      assert isinstance(cnn_model, tf.keras.Sequential)
      assert transform_flag is True
      assert cnn_model.input_shape[1:] == image_shape
      assert cnn_model.output_shape[1] == num_classes
      assert isinstance(prediction, np.ndarray)
      assert check_predict_cnn_correct(model, dataset_to_validate)
  
imports: import numpy
  from test.unit.common_tests import is_predict_ignores_target
  from test.unit.tasks.test_classification import get_image_classification_data
  from fedot.core.operations.evaluation.operation_implementations.models.keras import FedotCNNImplementation, check_input_array, create_deep_cnn, fit_cnn, predict_cnn
label_desc: Use image node_id 4 for calling a global function with example usage: test_cnn_methods() without return types