code: def _create_data_source(shape, with_ids=False):
      data_inputs = np.random.random(shape).astype(np.float32)
      expected_outputs = np.random.random(shape).astype(np.float32)
      if with_ids:
          ids = list(range(len(data_inputs)))
          return ids, data_inputs, expected_outputs
      else:
          return data_inputs, expected_outputs
  
imports: import numpy
  import pytest
  from neuraxle.base import BaseTransformer
  from neuraxle.base import ExecutionContext
  from neuraxle.base import ForceHandleMixin
  from neuraxle.data_container import EOT
  from neuraxle.data_container import DataContainer
  from neuraxle.hyperparams.space import HyperparameterSamples
  from neuraxle.pipeline import Pipeline
  from neuraxle.steps.misc import FitCallbackStep, TapeCallbackFunction
  from neuraxle.steps.numpy import MultiplyByN
  from neuraxle.steps.output_handlers import IdsInputAndOutputTransformerWrapper, OutputTransformerWrapper
label_desc: Use image node_id 10 for calling a global function with example usage: _create_data_source(shape, with_ids) and returns: ids,  data_inputs,  expected_outputs, data_inputs,  expected_outputs