class_docstr: Base output transformer step that can modify ids, data inputs, and expected_outputs at the same time.
docstr: Handle inverse transform by updating the data inputs, and expected outputs inside the data container.
  
  :param context: execution context
  :param data_container:
  :return:
docstr_tok: ["Handle","inverse","transform","by","updating","the","data","inputs",",","and","expected","outputs","inside","the","data","container",".",":","param","context",":","execution","context",":","param","data_container",":",":","return",":"]
code: def _transform_data_container(
      self, data_container: DACT, context: CX
  ) -> DACT:
      """
      Handle inverse transform by updating the data inputs, and expected outputs inside the data container.
  
      :param context: execution context
      :param data_container:
      :return:
      """
      di_eo = (
          data_container._ids,
          data_container.data_inputs,
          data_container.expected_outputs,
      )
      new_ids, new_data_inputs, new_expected_outputs = self.transform(
          di_eo
      )
      data_container.set_data_inputs(
          (new_ids, new_data_inputs, new_expected_outputs)
      )
      return data_container
  
imports: import copy
  from typing import Tuple
  from neuraxle.base import BaseStep
  from neuraxle.base import ExecutionContext
  from neuraxle.base import ForceHandleOnlyMixin, MetaStep, MixinForBaseTransformer
  from neuraxle.data_container import DataContainer
label_desc: Use image node_id 1 for calling the IdsAndInputAndOutputTransformerMixin obj's underlying member method code with example usage: obj._transform_data_container(data_container, context) and returns: data_container