code: def test_output_mode_full_probs():
      data = get_binary_classification_data()
      pipeline = pipeline_simple()
      train_data, test_data = train_test_data_setup(data, shuffle=True)
  
      pipeline.fit(input_data=train_data)
      results = pipeline.predict(
          input_data=test_data, output_mode="full_probs"
      )
      results_default = pipeline.predict(input_data=test_data)
      results_probs = pipeline.predict(
          input_data=test_data, output_mode="probs"
      )
  
      assert not np.array_equal(results_probs.predict, results.predict)
      assert np.array_equal(
          results_probs.predict, results_default.predict
      )
      assert results.predict.shape == (len(test_data.target), 2)
      assert results_probs.predict.shape == (len(test_data.target), 1)
  
imports: import os
  import numpy
  from sklearn.datasets import load_iris, make_classification
  from sklearn.metrics import roc_auc_score
  from examples.simple.classification.image_classification_problem import run_image_classification_problem
  from fedot.core.data.data import InputData
  from fedot.core.data.data_split import train_test_data_setup
  from fedot.core.data.supplementary_data import SupplementaryData
  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.integration.models.test_model import classification_dataset_with_redundant_features
label_desc: Use image node_id 10 for calling a global function with example usage: test_output_mode_full_probs() without return types