code: def _create_data_source():
      data_inputs = np.random.random((25, 50)).astype(np.float32)
      expected_outputs = (np.random.random((25,)) > 0.5).astype(
          np.int32
      )
      return data_inputs, expected_outputs
  
imports: import os
  from typing import Callable, Optional
  import numpy
  import pytest
  from neuraxle.base import BaseStep
  from neuraxle.base import ExecutionContext
  from neuraxle.base import Identity, NonFittableMixin
  from neuraxle.data_container import DataContainer
  from neuraxle.hyperparams.distributions import Boolean, Choice, LogUniform, RandInt
  from neuraxle.hyperparams.space import HyperparameterSpace
  from neuraxle.metaopt.auto_ml import AutoML, RandomSearchSampler
  from neuraxle.metaopt.callbacks import EarlyStoppingCallback, MetricCallback, ScoringCallback
  from neuraxle.metaopt.context import AutoMLContext
  from neuraxle.metaopt.data.vanilla import ScopedLocation
  from neuraxle.metaopt.repositories.repo import HyperparamsRepository, VanillaHyperparamsRepository
  from neuraxle.metaopt.validation import ValidationSplitter
  from neuraxle.pipeline import Pipeline
  from neuraxle.steps.numpy import NumpyRavel
  from neuraxle.steps.output_handlers import OutputTransformerWrapper
  from neuraxle.steps.sklearn import SKLearnWrapper
  from sklearn.linear_model import LogisticRegression
  from sklearn.metrics import accuracy_score, mean_squared_error
  from sklearn.preprocessing import StandardScaler
  from testing_neuraxle.metaopt.test_automl_repositories import CX_WITH_REPO_CTORS, TmpDir
label_desc: Use image node_id 1 for calling a global function with example usage: _create_data_source() and returns: data_inputs,  expected_outputs