code: def transform(self, data_inputs):
      ids, data_inputs, expected_outputs = data_inputs
  
      if not isinstance(data_inputs, np.ndarray):
          data_inputs = np.array(data_inputs)
  
      if not isinstance(expected_outputs, np.ndarray):
          expected_outputs = np.array(expected_outputs)
  
      return (
          ids,
          data_inputs * self.hyperparams["multiply_by"],
          expected_outputs * self.hyperparams["multiply_by"],
      )
  
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 2 for calling the MultiplyByNInputAndOutput obj's underlying member method code with example usage: obj.transform(data_inputs) and returns: ids,  unknown,  unknown