class_docstr: Wrapper step to transform both ids, data inputs, and expected output at the same time using classical fit and transform methods.
  It sends the data_inputs, and the expected_outputs to the wrapped step so that it can transform them.
  
  .. seealso::
      :class:`~neuraxle.base.BaseStep`,
      :class:`~neuraxle.base.ForceHandleOnlyMixin`
docstr: Handle inverse transform by passing the data inputs, and the expected outputs to the wrapped step inverse transform method.
  Update the expected outputs with the outputs.
  
  :param context: execution context
  :param data_container:
  :return: data container
  :rtype: DataContainer
docstr_tok: ["Handle","inverse","transform","by","passing","the","data","inputs",",","and","the","expected","outputs","to","the","wrapped","step","inverse","transform","method",".","Update","the","expected","outputs","with","the","outputs",".",":","param","context",":","execution","context",":","param","data_container",":",":","return",":","data","container",":","rtype",":","DataContainer"]
code: def handle_inverse_transform(
      self, data_container: DACT, context: CX
  ) -> DACT:
      """
      Handle inverse transform by passing the data inputs, and the expected outputs to the wrapped step inverse transform method.
      Update the expected outputs with the outputs.
  
      :param context: execution context
      :param data_container:
      :return: data container
      :rtype: DataContainer
      """
      output_data_container = self.wrapped.handle_inverse_transform(
          DACT(
              ids=data_container._ids,
              data_inputs=(
                  data_container._ids,
                  data_container.data_inputs,
                  data_container.expected_outputs,
              ),
              expected_outputs=None,
          ),
          context.push(self.wrapped),
      )
  
      return output_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 5 for calling the IdsInputAndOutputTransformerWrapper obj's underlying member method code with example usage: obj.handle_inverse_transform(data_container, context) and returns: output_data_container